OpenRaiser/Intern-Atlas huggingface dataset:
Geometry and Generation of a New Graph Planarity Game
Venue: arXiv cs.CG 2019
Year: 2019

We introduce a new abstract graph game, Swap Planarity, where the goal is to reach a state without edge intersections and a move consists of swapping the locations of two vertices connected by an edge. We analyze this puzzle game using concepts from graph theory and graph drawing, computational geometry, and complexity. Furthermore, we specify quality criteria for puzzle instances, and describe a method to generate high-quality instances. We also report on experiments that show how well this generation process works.
The fault-tolerant cluster-sending problem
Venue: arXiv cs.DC 2019
Year: 2019

The development of fault-tolerant distributed systems that can tolerate Byzantine behavior has traditionally been focused on consensus protocols, which support fully-replicated designs. For the development of more sophisticated high-performance Byzantine distributed systems, more specialized fault-tolerant communication primitives are necessary, however. In this paper, we identify an essential communication primitive and study it in depth. In specifics, we formalize the cluster-sending problem, the problem of sending a message from one Byzantine cluster to another Byzantine cluster in a reliable manner. We not only formalize this fundamental problem, but also establish lower bounds on the complexity of this problem under crash failures and Byzantine failures. Furthermore, we develop practical cluster-sending protocols that meet these lower bounds and, hence, have optimal complexity. As such, our work provides a strong foundation for the further exploration of novel designs that address challenges encountered in fault-tolerant distributed systems.
Minimum Energy Analysis for Robust Gaussian Joint Source-Channel Coding with a Square-Law Profile
Venue: arXiv cs.IT 2019
Year: 2019

A distortion-noise profile is a function indicating the maximum allowed source distortion value for each noise level in the channel. In this paper, the minimum energy required to achieve a distortion noise profile is studied for Gaussian sources which are transmitted robustly over Gaussian channels. We provide improved lower and upper bounds for the minimum energy behavior of the square-law profile using a family of lower bounds and our proposed coding scheme.
Constructive asymptotic bounds of locally repairable codes via function fields
Venue: arXiv cs.IT 2019
Year: 2019

Locally repairable codes have been investigated extensively in recent years due to practical applications in distributed and cloud storage systems. However, there are few asymptotical constructions of locally repairable codes in the literature. In this paper, we provide an explicit asymptotic construction of locally repairable codes over arbitrary finite fields from local expansions of functions at a rational place. This construction gives a Tsfasman-Vladut-Zink type bound for locally repairable codes. Its main advantage is that there are no constraints on both locality and alphabet size. Furthermore, we show that the Gilbert-Varshamov type bound on locally repairable codes over non-prime finite fields can be exceeded for sufficiently large alphabet size.
Image Steganography using Gaussian Markov Random Field Model
Venue: arXiv cs.MM 2019
Year: 2019

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependences among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers.
A Fast Content-Based Image Retrieval Method Using Deep Visual Features
Venue: arXiv cs.CV 2019
Year: 2019

Fast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.
Revisiting Feature Alignment for One-stage Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Recently, one-stage object detectors gain much attention due to their simplicity in practice. Its fully convolutional nature greatly reduces the difficulty of training and deployment compared with two-stage detectors which require NMS and sorting for the proposal stage. However, a fundamental issue lies in all one-stage detectors is the misalignment between anchor boxes and convolutional features, which significantly hinders the performance of one-stage detectors. In this work, we first reveal the deep connection between the widely used im2col operator and the RoIAlign operator. Guided by this illuminating observation, we propose a RoIConv operator which aligns the features and its corresponding anchors in one-stage detection in a principled way. We then design a fully convolutional AlignDet architecture which combines the flexibility of learned anchors and the preciseness of aligned features. Specifically, our AlignDet achieves a state-of-the-art mAP of 44.1 on the COCO test-dev with ResNeXt-101 backbone.
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
Venue: arXiv cs.IR 2019
Year: 2019

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.
Model Decay in Long-Term Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Updating the tracker model with adverse bounding box predictions adds an unavoidable bias term to the learning. This bias term, which we refer to as model decay, offsets the learning and causes tracking drift. While its adverse affect might not be visible in short-term tracking, accumulation of this bias over a long-term can eventually lead to a permanent loss of the target. In this paper, we look at the problem of model bias from a mathematical perspective. Further, we briefly examine the effect of various sources of tracking error on model decay, using a correlation filter (ECO) and a Siamese (SINT) tracker. Based on observations and insights, we propose simple additions that help to reduce model decay in long-term tracking. The proposed tracker is evaluated on four long-term and one short term tracking benchmarks, demonstrating superior accuracy and robustness, even in 30 minute long videos.
Thoth: Improved Rapid Serial Visual Presentation using Natural Language Processing
Venue: arXiv cs.CL 2019
Year: 2019

Thoth is a tool designed to combine many different types of speed reading technology. The largest insight is using natural language parsing for more optimal rapid serial visual presentation and more effective reading information.
Innovative ranking strategy for IPL team formation
Venue: arXiv cs.SI 2019
Year: 2019

Indian Premier League (IPL) is a tournament of twenty over cricket matches. Teams of this tournament are selected via an auction from a pool of players. Each team employs a think-tank to build the best possible team. Few studies have been performed to automate the process of team selection. However, those studies mostly concentrate either on the current form of the players, or their long term performance. In this paper, we have (i) selected traditional features as well as determined some derived features, which are generated from the traditional features, for batsmen and bowlers, (ii) formulated heuristics for clustering batsmen into openers, middle-order batsmen and finishers, (iii) formulated heuristics for relative ranking of batsmen and bowlers considering the current performance as well as the experience of each player, and (iv) have proposed two greedy algorithms for team selection where the total credit point of the team and the number of players in each cluster is fixed. Our proposed ranking scheme and algorithm not only determines the best possible team but can also determine the best alternate player if one of the target players is unavailable.
Unsupervised Context Retrieval for Long-tail Entities
Venue: arXiv cs.IR 2019
Year: 2019

Monitoring entities in media streams often relies on rich entity representations, like structured information available in a knowledge base (KB). For long-tail entities, such monitoring is highly challenging, due to their limited, if not entirely missing, representation in the reference KB. In this paper, we address the problem of retrieving textual contexts for monitoring long-tail entities. We propose an unsupervised method to overcome the limited representation of long-tail entities by leveraging established entities and their contexts as support information. Evaluation on a purpose-built test collection shows the suitability of our approach and its robustness for out-of-KB entities.
Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian
Venue: arXiv cs.CL 2019
Year: 2019

Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e. aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Persian is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of public dataset on aspect-based sentiment analysis for Persian. This paper provides a manually annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some state-of-the-art aspect-based sentiment analysis methods with a focus on deep learning, on Pars-ABSA. The obtained results are impressive compared to similar English state-of-the-art.
Dialogue Act Classification in Group Chats with DAG-LSTMs
Venue: arXiv cs.CL 2019
Year: 2019

Dialogue act (DA) classification has been studied for the past two decades and has several key applications such as workflow automation and conversation analytics. Researchers have used, to address this problem, various traditional machine learning models, and more recently deep neural network models such as hierarchical convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In this paper, we introduce a new model architecture, directed-acyclic-graph LSTM (DAG-LSTM) for DA classification. A DAG-LSTM exploits the turn-taking structure naturally present in a multi-party conversation, and encodes this relation in its model structure. Using the STAC corpus, we show that the proposed method performs roughly 0.8% better in accuracy and 1.2% better in macro-F1 score when compared to existing methods. The proposed method is generic and not limited to conversation applications.
Semi-Automatic Labeling for Deep Learning in Robotics
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen to define initial object bounding box, to create large labeled datasets with minimal human intervention. By removing the burden of generating annotated data from humans, we make the Deep Learning technique applied to computer vision, that typically requires very large datasets, truly automated and reliable. With the ARS pipeline, we created effortlessly two novel datasets, one on electromechanical components (industrial scenario) and one on fruits (daily-living scenario), and trained robustly two state-of-the-art object detectors, based on convolutional neural networks, such as YOLO and SSD. With respect to the conventional manual annotation of 1000 frames that takes us slightly more than 10 hours, the proposed approach based on ARS allows annotating 9 sequences of about 35000 frames in less than one hour, with a gain factor of about 450. Moreover, both the precision and recall of object detection is increased by about 15\% with respect to manual labeling. All our software is available as a ROS package in a public repository alongside the novel annotated datasets.
Backronym
Venue: arXiv cs.CY 2019
Year: 2019

The field of Machine Learning research is divided into subject areas, where each area tries to solve a specific problem, using specific methods. In recent years, borders have almost been erased, and many areas inherit methods from other areas. This trend leads to better results and the number of papers in the field is growing every year. The problem is that the amount of information is also growing, and many methods remain unknown in a large number of papers. In this work, we propose the concept of inheritance between machine learning models, which allows conducting research, processing much less information, and pay attention to previously unnoticed models. We hope that this project will allow researchers to find ways to improve their ideas. In addition, it can be used by researchers to publish their methods too. Project is available by link: https://www.infornopolitan.xyz/backronym
Concury: A Fast and Light-weighted Software Load Balancer
Venue: arXiv cs.NI 2019
Year: 2019

A load balancer (LB) is a vital network function for cloud services to balance the load amongst resources. Stateful software LBs that run on commodity servers provides flexibility, cost-efficiency, and packet consistency. However current designs have two main limitations: 1) states are stored as digests which may cause packet inconsistency due to digest collisions; 2) the data plane needs to update for every new connection, and frequent updates hurt throughput and packet consistency. In this work, we present a new software stateful LB called Concury, which is the first solution to solve these problems. The key innovation of Concury is an algorithmic approach to store and look up large network states with frequent connection arrivals, which is succinct in memory cost, consistent under network changes, and incurs infrequent data plane updates. The evaluation results show that the Concury algorithm provides 4x throughput and consumes less memory compared to other LB algorithms, while providing weighted load balancing and false-hit freedom, for both real and synthetic data center traffic. We implement Concury as a prototype system deployed in CloudLab and show that the throughput of Concury on a single thread can reach 62.5% of the maximum capacity of two 10GbE NICs and that on two threads can reach the maximum capacity.
AppMine: Behavioral Analytics for Web Application Vulnerability Detection
Venue: arXiv cs.CR 2019
Year: 2019

Web applications in widespread use have always been the target of large-scale attacks, leading to massive disruption of services and financial loss, as in the Equifax data breach. It has become common practice to deploy web application in containers like Docker for better portability and ease of deployment. We design a system called AppMine for lightweight monitoring of web applications running in Docker containers and detection of unknown web vulnerabilities. AppMine is an unsupervised learning system, trained only on legitimate workloads of web application, to detect anomalies based on either traditional models (PCA and one-class SVM), or more advanced neural-network architectures (LSTM). In our evaluation, we demonstrate that the neural network model outperforms more traditional methods on a range of web applications and recreated exploits. For instance, AppMine achieves average AUC scores as high as 0.97 for the Apache Struts application (with the CVE-2017-5638 exploit used in the Equifax breach), while the AUC scores for PCA and one-class SVM are 0.81 and 0.83, respectively.
Using the Makerspace to Create Educational Open-source Software for Electrical Circuits: A Learning Experience
Venue: arXiv cs.HC 2019
Year: 2019

Virtual learning environments are a useful modality for engaging students in the classroom by affording them a sense of presence and immersion. The motivation of this project was to create an open-source augmented reality electrical circuit application for use in lower division engineering courses to teach students about electricity fundamentals. Softwares that are readily available for use on virtual and augmented reality devices do not typically apply to all disciplines and do not necessarily have a pedagogical or accessibility focus. Considering this lack of appropriate educational applications for the current virtual and augmented reality devices, a team of interdisciplinary students was assembled to create such software. With extensive usability studies, the application was designed for quick adoption and improve accessibility by providing multimodal access such as voice assistant, gray scaling for depth perception and daltonize the app. The software is available as part of VITaL Laboratory, Sonoma State University.
AttentionBoost: Learning What to Attend by Boosting Fully Convolutional Networks
Venue: arXiv cs.CV 2019
Year: 2019

Dense prediction models are widely used for image segmentation. One important challenge is to sufficiently train these models to yield good generalizations for hard-to-learn pixels. A typical group of such hard-to-learn pixels are boundaries between instances. Many studies have proposed to give specific attention to learning the boundary pixels. They include designing multi-task networks with an additional task of boundary prediction and increasing the weights of boundary pixels' predictions in the loss function. Such strategies require defining what to attend beforehand and incorporating this defined attention to the learning model. However, there may exist other groups of hard-to-learn pixels and manually defining and incorporating the appropriate attention for each group may not be feasible. In order to provide a more attainable and scalable solution, this paper proposes AttentionBoost, which is a new multi-attention learning model based on adaptive boosting. AttentionBoost designs a multi-stage network and introduces a new loss adjustment mechanism for a dense prediction model to adaptively learn what to attend at each stage directly on image data without necessitating any prior definition about what to attend. This mechanism modulates the attention of each stage to correct the mistakes of previous stages, by adjusting the loss weight of each pixel prediction separately with respect to how accurate the previous stages are on this pixel. This mechanism enables AttentionBoost to learn different attentions for different pixels at the same stage, according to difficulty of learning these pixels, as well as multiple attentions for the same pixel at different stages, according to confidence of these stages on their predictions for this pixel. Using gland segmentation as a showcase application, our experiments demonstrate that AttentionBoost improves the results of its counterparts.
Deep learning research landscape & roadmap in a nutshell: past, present and future -- Towards deep cortical learning
Venue: arXiv cs.NE 2019
Year: 2019

The past, present and future of deep learning is presented in this work. Given this landscape & roadmap, we predict that deep cortical learning will be the convergence of deep learning & cortical learning which builds an artificial cortical column ultimately.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Venue: arXiv cs.MM 2019
Year: 2019

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it typically suffers from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.