OpenRaiser/Intern-Atlas huggingface dataset:
Structured Group Local Sparse Tracker
Venue: arXiv cs.CV 2019
Year: 2019

Sparse representation is considered as a viable solution to visual tracking. In this paper, we propose a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularization term. To solve the optimization model, we propose an efficient numerical algorithm consisting of two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmarks of challenging image sequences demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.
Online PCB Defect Detector On A New PCB Defect Dataset
Venue: arXiv cs.CV 2019
Year: 2019

Previous works for PCB defect detection based on image difference and image processing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patterns or over-sensitivity about some hyper-parameters. In this work, we design a deep model that accurately detects PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features of a large range of resolutions, which are merged by group to predict PCB defect of corresponding scales. To train the deep model, a dataset is established, namely DeepPCB, which contains 1,500 image pairs with annotations including positions of 6 common types of PCB defects. Experiment results validate the effectiveness and efficiency of the proposed model by achieving $98.6\%$ mAP @ 62 FPS on DeepPCB dataset. This dataset is now available at: https://github.com/tangsanli5201/DeepPCB.
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data
Venue: arXiv cs.CL 2019
Year: 2019

With the increasing importance of online communities, discussion forums, and customer reviews, Internet "trolls" have proliferated thereby making it difficult for information seekers to find relevant and correct information. In this paper, we consider the problem of detecting and identifying Internet trolls, almost all of which are human agents. Identifying a human agent among a human population presents significant challenges compared to detecting automated spam or computerized robots. To learn a troll's behavior, we use contextual anomaly detection to profile each chat user. Using clustering and distance-based methods, we use contextual data such as the group's current goal, the current time, and the username to classify each point as an anomaly. A user whose features significantly differ from the norm will be classified as a troll. We collected 38 million data points from the viral Internet fad, Twitch Plays Pokemon. Using clustering and distance-based methods, we develop heuristics for identifying trolls. Using MapReduce techniques for preprocessing and user profiling, we are able to classify trolls based on 10 features extracted from a user's lifetime history.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
Venue: arXiv cs.CV 2019
Year: 2019

Image colorization achieves more and more realistic results with the increasing computation power of recent deep learning techniques. It becomes more difficult to identify the fake colorized images by human eyes. In this work, we propose a novel forensic method to distinguish between natural images (NIs) and colorized images (CIs) based on convolutional neural network (CNN). Our method is able to achieve high classification accuracy and cope with the challenging scenario of blind detection, i.e., no training sample is available from "unknown" colorization algorithm that we may encounter during the testing phase. This blind detection performance can be regarded as a generalization performance. First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods. Furthermore, we design a new branch, which analyzes smaller regions of extracted features, and insert it into the above base network. Consequently, our network can not only improve the classification accuracy, but also enhance the generalization in the vast majority of cases. To further improve the performance of blind detection, we propose to automatically construct negative samples through linear interpolation of paired natural and colorized images. Then, we progressively insert these negative samples into the original training dataset and continue to train the network. Experimental results demonstrate that our method can achieve stable and high generalization performance when tested against different state-of-the-art colorization algorithms.
A Lower Bound for Primality of Finite Languages
Venue: arXiv cs.FL 2019
Year: 2019

A regular language $L$ is said to be prime, if it is not the product of two non-trivial languages. Martens et al. settled the exact complexity of deciding primality for deterministic finite automata in 2010. For finite languages, Mateescu et al. and Wieczorek suspect the $\mathrm{NP}\text{ - }completeness$ of primality, but no actual bounds are given. Using techniques of Martens et al., we prove the $\mathrm{NP}$ lower bound and give a $Π_{2}^{\mathrm{P}}$ upper bound for deciding primality of finite languages given as deterministic finite automata.
Fully-Featured Attribute Transfer
Venue: arXiv cs.CV 2019
Year: 2019

Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years. However, most of the existing methods lack the ability to de-correlate the target attributes and irrelevant information, i.e., the other attributes and background information, thus often suffering from blurs and artifacts. To address these issues, we propose a novel Attribute Manifold Encoding GAN (AME-GAN) for fully-featured attribute transfer, which can modify and adjust every detail in the images. Specifically, our method divides the input image into image attribute part and image background part on manifolds, which are controlled by attribute latent variables and background latent variables respectively. Through enforcing attribute latent variables to Gaussian distributions and background latent variables to uniform distributions respectively, the attribute transfer procedure becomes controllable and image generation is more photo-realistic. Furthermore, we adopt a conditional multi-scale discriminator to render accurate and high-quality target attribute images. Experimental results on three popular datasets demonstrate the superiority of our proposed method in both performances of the attribute transfer and image generation quality.
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
Venue: arXiv cs.CV 2019
Year: 2019

Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel approach that bridges the domain gap by projecting the source and target domains into a common association space through an unsupervised ``cross-grafted representation stacking'' (CGRS) mechanism. Specifically, we construct variational auto-encoders (VAE) for the two domains, and form bidirectional associations by cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial networks (GAN) are employed for label alignment (LA), mapping the target domain data to the known label space of the source domain. The overall adaptation process hence consists of three phases: feature representation learning by VAEs, association generation, and association label alignment by GANs. Experimental results demonstrate that our CGRS-LA approach outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks.
Semantically Interpretable and Controllable Filter Sets
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion. Each semantic filter learns a visually interpretable local structure in conjunction with other filters. The significance of learning these interpretable filter sets is demonstrated on two contrasting applications. The first application is image recognition under progressive decolorization, in which recognition algorithms should be color-insensitive to achieve a robust performance. The second application is image quality assessment where objective methods should be sensitive to color degradations. In the proposed work, the sensitivity and lack thereof are controlled by weighing the semantic filters based on the local structures they represent. To validate the proposed approach, we utilize the CURE-TSR dataset for image recognition and the TID 2013 dataset for image quality assessment. We show that the proposed semantic filter set achieves state-of-the-art performances in both datasets while maintaining its robustness across progressive distortions.
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning models are vulnerable to adversarial examples, which poses an indisputable threat to their applications. However, recent studies observe gradient-masking defenses are self-deceiving methods if an attacker can realize this defense. In this paper, we propose a new defense method based on appending information. We introduce the Aux Block model to produce extra outputs as a self-ensemble algorithm and analytically investigate the robustness mechanism of Aux Block. We have empirically studied the efficiency of our method against adversarial examples in two types of white-box attacks, and found that even in the full white-box attack where an adversary can craft malicious examples from defense models, our method has a more robust performance of about 54.6% precision on Cifar10 dataset and 38.7% precision on Mini-Imagenet dataset. Another advantage of our method is that it is able to maintain the prediction accuracy of the classification model on clean images, and thereby exhibits its high potential in practical applications
Optimal Sequence and Performance for Desired User in Asynchronous CDMA System
Venue: arXiv cs.IT 2019
Year: 2019

We consider asynchronous CDMA systems in no-fading environments with a particular focus on a certain user. This certain user is called a desired user in this paper. In such a situation, an optimal sequence, maximum Signal-to-Interference plus Noise Ratio (SINR) and the maximum capacity for a desired user are derived with other spreading sequences being given and fixed. In addition, the maximum SINR and the optimal sequence for a desired user are written in terms of the minimum eigenvalue and the corresponding eigenvector of a matrix, respectively. Since it is not straightforward to obtain an explicit form of the maximum SINR, we evaluate SINR and obtain the lower and upper bounds of the maximum SINR. From these bounds, the maximum SINR may get larger as the quantities written in terms of quadratic forms of other spreading sequences decrease. Further, we propose a method to obtain spreading sequences for all the users which achieve large SINRs. The performance of our proposed method is numerically verified.
Schema Validation and Evolution for Graph Databases
Venue: arXiv cs.DB 2019
Year: 2019

Despite the maturity of commercial graph databases, little consensus has been reached so far on the standardization of data definition languages (DDLs) for property graphs (PG). The discussion on the characteristics of PG schemas is ongoing in many standardization and community groups. Although some basic aspects of a schema are already present in Neo4j 3.5, like in most commercial graph databases, full support is missing allowing to constraint property graphs with more or less flexibility. In this paper, we focus on two different perspectives from which a PG schema should be considered, as being descriptive or prescriptive, and we show how it would be possible to switch from one to another as the application under development gains more stability. Apart from proposing concise schema DDL inspired by Cypher syntax, we show how schema validation can be enforced through homomorphisms between PG schemas and PG instances; and how schema evolution can be described through the use of graph rewriting operations. Our prototypical implementation demonstrates feasibility and shows the need of offering high-level query primitives to accommodate flexible graph schema requirements as showcased in our work.
Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning
Venue: arXiv cs.DC 2019
Year: 2019

As the models and the datasets to train deep learning (DL) models scale, system architects are faced with new challenges, one of which is the memory capacity bottleneck, where the limited physical memory inside the accelerator device constrains the algorithm that can be studied. We propose a memory-centric deep learning system that can transparently expand the memory capacity available to the accelerators while also providing fast inter-device communication for parallel training. Our proposal aggregates a pool of memory modules locally within the device-side interconnect, which are decoupled from the host interface and function as a vehicle for transparent memory capacity expansion. Compared to conventional systems, our proposal achieves an average 2.8x speedup on eight DL applications and increases the system-wide memory capacity to tens of TBs.
Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks
Venue: arXiv cs.IR 2019
Year: 2019

The objective of this research is to enhance performance of Stochastic Gradient Descent (SGD) algorithm in text classification. In our research, we proposed using SGD learning with Grid-Search approach to fine-tuning hyper-parameters in order to enhance the performance of SGD classification. We explored different settings for representation, transformation and weighting features from the summary description of terrorist attacks incidents obtained from the Global Terrorism Database as a pre-classification step, and validated SGD learning on Support Vector Machine (SVM), Logistic Regression and Perceptron classifiers by stratified 10-K-fold cross-validation to compare the performance of different classifiers embedded in SGD algorithm. The research concludes that using a grid-search to find the hyper-parameters optimize SGD classification, not in the pre-classification settings only, but also in the performance of the classifiers in terms of accuracy and execution time.
Quality of Life Assessment of Diabetic patients from health-related blogs
Venue: arXiv cs.CY 2019
Year: 2019

Motivations: People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. The increasing availability of these social traces presents an interesting opportunity to enhance timeliness and efficiency of care. By collecting, analyzing and exploiting this information, it is possible to modify or in any case significantly improve our knowledge on the manifestation of a pathology and obtain a more detailed and nuanced vision of patients' experience, that we call the "social phenotype" of diseases. Materials and methods: In this paper we present a data analytic framework to represent, extract and analyze the social phenotype of diseases. To show the effectiveness of our methodology we presents a detailed case study on diabetes. First, we create a high quality data sample of diabetic patients' messages, extracted from popular medical forums during more than 10 years. Next, we use a topic extraction techniques based on latent analysis and word embeddings, to identify the main complications, the frequently reported symptoms and the common concerns of these patients. Results: We show that a freely manifested perception of a disease can be noticeably different from what is inferred from questionnaires, surveys and other common methodologies used to measure the impact of a disease on the patients' quality of life. In our case study on diabetes, we found that issues reported to have a daily impact on diabetic patients are diet, glycemic control, drugs and clinical tests. These problems are not commonly considered in Quality of Life assessments, since they are not perceived by doctors as representing severe limitations.
Binary Debloating for Security via Demand Driven Loading
Venue: arXiv cs.CR 2019
Year: 2019

Modern software systems heavily use C/C++ based libraries. Because of the weak memory model of C/C++, libraries may suffer from vulnerabilities which can expose the applications to potential attacks. For example, a very large number of return oriented programming gadgets exist in glibc that allow stitching together semantically valid but malicious Turing-complete programs. In spite of significant advances in attack detection and mitigation, full defense is unrealistic against an ever-growing set of possibilities for generating such malicious programs. In this work, we create a defense mechanism by debloating libraries to reduce the dynamic functions linked so that the possibilities of constructing malicious programs diminishes significantly. The key idea is to locate each library call site within an application, and in each case to load only the set of library functions that will be used at that call site. This approach of demand-driven loading relies on an input-aware oracle that predicts a near-exact set of library functions needed at a given call site during the execution. The predicted functions are loaded just in time, and the complete call chain (of function bodies) inside the library is purged after returning from the library call back into the application. We present a decision-tree based predictor, which acts as an oracle, and an optimized runtime system, which works directly with library binaries like GNU libc and libstdc++. We show that on average, the proposed scheme cuts the exposed code surface of libraries by 97.2%, reduces ROP gadgets present in linked libraries by 97.9%, achieves a prediction accuracy in most cases of at least 97%, and adds a small runtime overhead of 18% on all libraries (16% for glibc, 2% for others) across all benchmarks of SPEC 2006, suggesting this scheme is practical.
Extending Upward Planar Graph Drawings
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we study the computational complexity of the Upward Planarity Extension problem, which takes in input an upward planar drawing $Γ_H$ of a subgraph $H$ of a directed graph $G$ and asks whether $Γ_H$ can be extended to an upward planar drawing of $G$. Our study fits into the line of research on the extensibility of partial representations, which has recently become a mainstream in Graph Drawing. We show the following results. First, we prove that the Upward Planarity Extension problem is NP-complete, even if $G$ has a prescribed upward embedding, the vertex set of $H$ coincides with the one of $G$, and $H$ contains no edge. Second, we show that the Upward Planarity Extension problem can be solved in $O(n \log n)$ time if $G$ is an $n$-vertex upward planar $st$-graph. This result improves upon a known $O(n^2)$-time algorithm, which however applies to all $n$-vertex single-source upward planar graphs. Finally, we show how to solve in polynomial time a surprisingly difficult version of the Upward Planarity Extension problem, in which $G$ is a directed path or cycle with a prescribed upward embedding, $H$ contains no edges, and no two vertices share the same $y$-coordinate in $Γ_H$.
Increasing city safety awareness regarding disruptive traffic stream
Venue: arXiv cs.CY 2019
Year: 2019

Transportation systems serve the people in essence, in this study we focus in traffic information related to violation events to respond to safety requirements of the cities. Traffic violation events have an important role in city safety awareness and secure travel. In this work, we describe the use of knowledge discovery from traffic violation reports in combination with demographics approach using inductive logic programming to automatically extract knowledge about traffic violation behavior and their impact on the environment.
Big Data and Geospatial Analysis
Venue: arXiv cs.CY 2019
Year: 2019

Perhaps one of the mostly hotly debated topics in recent years has been the question of "GIS and Big Data". Much of the discussion has been about the data: huge volumes of 2D and 3D spatial data and spatio-temporal data are now being collected and stored; so how they can be accessed? and how can we map and interpret massive datasets in an effective manner? Less attention has been paid to questions regarding the analysis of Big Data, although this has risen up the agenda in recent times. Examples include the use of density analysis to represent map request events, with Esri demonstrating that (given sufficient resources) they can process and analyze large numbers of data point events using kernel density techniques within a very short timeframe (under a minute); data filtering (to extract subsets of data that are of particular interest); and data mining (broader than simple filtering). For real-time data, sequential analysis has also been successfully applied; in this case the data are received as a stream and are used to build up a dynamic map or to cumulatively generate statistical values that may be mapped and/or used to trigger events or alarms. To this extent the analysis is similar to that conducted on smaller datasets, but with data and processing architectures that are specifically designed to cope with the data volumes involved and with a focus on data exploration as a key mechanism for discovery. Miller and Goodchild (2014) have argued that considerable care is required when working with Big Data significant issues arise from each of the "four Vs of Big Data": the sheer Volume of data; the Velocity of data arrival; the Variety of forms of data and their origins; and the Veracity of such data. As such, geospatial research has had to adapt to harness new forms of data to validly represent real-world phenomena.
A Generalised Solution to Distributed Consensus
Venue: arXiv cs.DC 2019
Year: 2019

Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental primitive for constructing reliable distributed systems from unreliable components. The Paxos algorithm is synonymous with distributed consensus, yet it performs poorly in practice and is famously difficult to understand. In this paper, we re-examine the foundations of distributed consensus. We derive an abstract solution to consensus, which utilises immutable state for intuitive reasoning about safety. We prove that our abstract solution generalises over Paxos as well as the Fast Paxos and Flexible Paxos algorithms. The surprising result of this analysis is a substantial weakening to the quorum requirements of these widely studied algorithms.