OpenRaiser/Intern-Atlas huggingface dataset:
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
Venue: arXiv cs.CV 2019
Year: 2019

Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can extract high-level local features to find road regions from raw RGB data, such as Convolutional Neural Networks (CNN) and Fully Convolutional Networks (FCN). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose a siamesed fully convolutional networks (named as ``s-FCN-loc''), which is able to consider RGB-channel images, semantic contours and location priors simultaneously to segment road region elaborately. To be specific, the s-FCN-loc has two streams to process the original RGB images and contour maps respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: (1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions; (2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCN-loc to promote the detection performance effectively, which is easier than other traditional methods, namely different priors for different inputs (image patches); (3) The convergent speed of training s-FCN-loc model is 30\% faster than the original FCN, because of the guidance of highly structured contours. The proposed approach is evaluated on KITTI Road Detection Benchmark and One-Class Road Detection Dataset, and achieves a competitive result with state of the arts.
The Game of Tetris in Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

The game of Tetris is an important benchmark for research in artificial intelligence and machine learning. This paper provides a historical account of the algorithmic developments in Tetris and discusses open challenges. Handcrafted controllers, genetic algorithms, and reinforcement learning have all contributed to good solutions. However, existing solutions fall far short of what can be achieved by expert players playing without time pressure. Further study of the game has the potential to contribute to important areas of research, including feature discovery, autonomous learning of action hierarchies, and sample-efficient reinforcement learning.
Answer Set Solving exploiting Treewidth and its Limits
Venue: arXiv cs.LO 2019
Year: 2019

Parameterized algorithms have been subject to extensive research of recent years and allow to solve hard problems by exploiting a parameter of the corresponding problem instances. There, one goal is to devise algorithms, where the runtime is exponential exclusively in this parameter. One particular well-studied structural parameter is treewidth. Typically, a parameterized algorithm utilizing treewidth takes or computes a tree decomposition, which is an arrangement of a graph into a tree, and evaluates the problem in parts by dynamic programming on the tree decomposition. In our research, we want to exploit treewidth in the context of Answer Set Programming (ASP), a declarative modeling and solving framework, which has been successfully applied in several application domains and industries for years. So far, we presented algorithms for ASP for the full ASP-Core-2 syntax, which is competitive especially when it comes to counting answer sets. Since dynamic programming on tree decomposition lands itself well to counting, we designed a framework for projected model counting, which applies to ASP, abstract argumentation and even to problems higher in the polynomial hierarchy. Given standard assumptions in computational complexity, we established a novel methodology for showing lower bounds, and we showed that most worst-case runtimes of our algorithms cannot be significantly improved.
Internet, Social Media and Conflict Studies Can Greater Interdisciplinarity Solve the Analytical Deadlocks in Cybersecurity Research?
Venue: arXiv cs.CY 2019
Year: 2019

In recent years, computational research methods, digital trace data and online human interactions have contributed to the emergence of new technology-oriented sub-fields within International Relations (IR). Although the cybersecurity scholarship had an initial promise to be the primus inter pares among these emerging fields, the main thrust of this new methodological innovation came through the digital conflict studies sub-field. By integrating Internet and social media research tools and questions into its core topics of sub-national violence, terrorism and radical mobilization, digital conflict studies has recently succeeded in addressing some of the data validity and methodology problems faced by the cybersecurity scholarship. This article begins by briefly reviewing some of the persistent data and method-oriented hurdles faced by the cybersecurity scholarship. Then, it moves onto a more detailed account of how digital conflict studies have been addressing some of these deadlocks by focusing individually on the literature on onset, mobilization, targeting, intensity/duration and termination phases of conflicts. Ultimately, the article concludes with the suggestion that the cybersecurity scholarship could move past its own deadlocks by building more granular and dedicated research datasets and establishing mechanisms to share event data with the scientific community.
Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge
Venue: arXiv cs.CL 2019
Year: 2019

We present our 7th place solution to the Gendered Pronoun Resolution challenge, which uses BERT without fine-tuning and a novel augmentation strategy designed for contextual embedding token-level tasks. Our method anonymizes the referent by replacing candidate names with a set of common placeholder names. Besides the usual benefits of effectively increasing training data size, this approach diversifies idiosyncratic information embedded in names. Using same set of common first names can also help the model recognize names better, shorten token length, and remove gender and regional biases associated with names. The system scored 0.1947 log loss in stage 2, where the augmentation contributed to an improvements of 0.04. Post-competition analysis shows that, when using different embedding layers, the system scores 0.1799 which would be third place.
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices
Venue: arXiv cs.CV 2019
Year: 2019

To achieve lightweight object detectors for deployment on the edge devices, an effective model compression pipeline is proposed in this paper. The compression pipeline consists of automatic channel pruning for the backbone, fixed channel deletion for the branch layers and knowledge distillation for the guidance learning. As results, the Resnet50-v1d is auto-pruned and fine-tuned on ImageNet to attain a compact base model as the backbone of object detector. Then, lightweight object detectors are implemented with proposed compression pipeline. For instance, the SSD-300 with model size=16.3MB, FLOPS=2.31G, and mAP=71.2 is created, revealing a better result than SSD-300-MobileNet.
Computing a Data Dividend
Venue: arXiv cs.GT 2019
Year: 2019

Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods to assess the value of individual data samples, and of sets of samples, to apportion value among different data contributors. We use Shapley values for individual samples and Owen values for combined samples, and show that these values can be computed in polynomial time in spite of their definitions having numbers of terms that are exponential in the number of samples.
Accessibility Evaluation of Computer Based Tests
Venue: arXiv cs.HC 2019
Year: 2019

Computer-based tests (CBTs) play an important role in the professional career of any person. Universities use CBTs for admissions. Further, many large courses use CBTs for evaluation and grading. Almost all software companies use CBTs to offer jobs. However, many of these CBTs do not take attention to the accessibility barriers for persons with disabilities, specifically visually impaired persons. In this paper, we present a study of accessibility barriers in various CBTs as faced by visually impaired persons in India. These barriers have been identified by a questionnaire survey approach. Our analysis of the responses shows that most CBTs do not meet the expectations of visually impaired persons. We conclude the paper with some recommendations to improve accessibility.
Characterizing and Detecting CUDA Program Bugs
Venue: arXiv cs.SE 2019
Year: 2019

While CUDA has become a major parallel computing platform and programming model for general-purpose GPU computing, CUDA-induced bug patterns have not yet been well explored. In this paper, we conduct the first empirical study to reveal important categories of CUDA program bug patterns based on 319 bugs identified within 5 popular CUDA projects in GitHub. Our findings demonstrate that CUDA-specific characteristics may cause program bugs such as synchronization bugs that are rather difficult to detect. To efficiently detect such synchronization bugs, we establish the first lightweight general CUDA bug detection framework, namely Simulee, to simulate CUDA program execution by interpreting the corresponding llvm bytecode and collecting the memory-access information to automatically detect CUDA synchronization bugs. To evaluate the effectiveness and efficiency of Simulee, we conduct a set of experiments and the experimental results suggest that Simulee can detect 20 out of the 27 studied synchronization bugs and successfully detects 26 previously unknown synchronization bugs, 10 of which have been confirmed by the developers.
Investigating kernel shapes and skip connections for deep learning-based harmonic-percussive separation
Venue: arXiv cs.SD 2019
Year: 2019

In this paper we propose an efficient deep learning encoder-decoder network for performing Harmonic-Percussive Source Separation (HPSS). It is shown that we are able to greatly reduce the number of model trainable parameters by using a dense arrangement of skip connections between the model layers. We also explore the utilisation of different kernel sizes for the 2D filters of the convolutional layers with the objective of allowing the network to learn the different time-frequency patterns associated with percussive and harmonic sources more efficiently. The training and evaluation of the separation has been done using the training and test sets of the MUSDB18 dataset. Results show that the proposed deep network achieves automatic learning of high-level features and maintains HPSS performance at a state-of-the-art level while reducing the number of parameters and training time.
Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector
Venue: arXiv cs.CL 2019
Year: 2019

We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our best-performing model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models
Venue: arXiv cs.CL 2019
Year: 2019

Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.
Neural Machine Translation with Recurrent Highway Networks
Venue: arXiv cs.CL 2019
Year: 2019

Recurrent Neural Networks have lately gained a lot of popularity in language modelling tasks, especially in neural machine translation(NMT). Very recent NMT models are based on Encoder-Decoder, where a deep LSTM based encoder is used to project the source sentence to a fixed dimensional vector and then another deep LSTM decodes the target sentence from the vector. However there has been very little work on exploring architectures that have more than one layer in space(i.e. in each time step). This paper examines the effectiveness of the simple Recurrent Highway Networks(RHN) in NMT tasks. The model uses Recurrent Highway Neural Network in encoder and decoder, with attention .We also explore the reconstructor model to improve adequacy. We demonstrate the effectiveness of all three approaches on the IWSLT English-Vietnamese dataset. We see that RHN performs on par with LSTM based models and even better in some cases.We see that deep RHN models are easy to train compared to deep LSTM based models because of highway connections. The paper also investigates the effects of increasing recurrent depth in each time step.
Deep Ordinal Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapting reinforcement learning problems to the use of ordinal rewards is presented and motivated. We show how to convert common reinforcement learning algorithms to an ordinal variation by the example of Q-learning and introduce Ordinal Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. Additionally, we run evaluations on problems provided by the OpenAI Gym framework, showing that our ordinal variants exhibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.
TryLinks: An interactive tutorial system for a cross-tier Web programming language
Venue: arXiv cs.PL 2019
Year: 2019

Links is a web programming language under development in Edinburgh aimed at simplifying web development. Conventional multi-tier applications involve programming in several languages for different layers, and the mismatches between these layers and abstractions need to be handled by the programmer, which can lead to costly errors or security vulnerabilities. In Links, programs combine all of the code of a web application in a single program, and the implementation generates appropriate JavaScript and HTML for the client, and SQL queries for the database. However, installing and using Links is non-trivial, making it difficult for new programmers to get started and learn about Links. This paper reports on a Web-based "TryLinks" system, allowing anyone to experiment with Links without going through the installation process. TryLinks was designed with two major functionalities: an interactive Links shell that teaches the basic syntax of Links and acts as a playground, as well as a short tutorial series on how Links is used in practical web development. Tutorials can also be created or modified by administrators. We present the design and implementation of TryLinks, and conclude with discussion of lessons learned from this project and remaining challenges for Web-based tutorials for Web programming languages.
Unsupervised Domain Adaptation using Graph Transduction Games
Venue: arXiv cs.LG 2019
Year: 2019

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.
Programmable Logic Arrays
Venue: arXiv cs.OH 2019
Year: 2019

Programmable logic arrays (PLAs) are traditional digital electronic devices. A PLA is a simple programmable logic device (SPLD) used to implement combinational logic circuits. A PLA has a set of programmable AND gates, which link to a set of programmable OR gates to produce an output. The AND-OR layout of a PLA allows for implementing logic functions that are in a sum-of-products form. PLAs are available in the market in different types. PLAs could be stand alone chips, or parts of bigger processing systems. Stand alone PLAs are available as mask programmable (MPLAs) and field programmable (FPLAs) devices. The attractions of PLAs that brought them to mainstream engineers include their simplicity, relatively small circuit area, predictable propagation delay, and ease of development. The powerful-but-simple property brought PLAs to rapid prototyping, synthesis, design optimization techniques, embedded systems, traditional computer systems, hybrid high-performance computing systems, etc. Indeed, there has been renewable interests in working with the simple AND-to-OR PLAs.
Logic Design
Venue: arXiv cs.OH 2019
Year: 2019

Electronic circuits can be separated into two groups, digital and analog circuits. Analog circuits operate on analog quantities that are continuous in value, whereas digital circuits operate on digital quantities that are discrete in value and limited in precision. In practice, most digital systems contain combinational circuits along with memory; these systems are known as sequential circuits. Sequential circuits are of two types: synchronous and asynchronous. In a synchronous sequential circuit, a clock signal is used at discrete instants of time to synchronize desired operations. Asynchronous sequential circuits do not require synchronizing clock pulses; however, the completion of an operation signals the start of the next operation in sequence. The basic logic design steps are generally identical for sequential and combinational circuits; these are specification, formulation, optimization, and the implementation of the optimized equations using a suitable hardware technology. The differences between sequential and combinational design steps appear in the details of each step. The minimization (optimization) techniques used in logic design range from simple (manual) to complex (automated). An example of manual optimization methods is the Karnough map (K-map). Indeed, hardware implementation technology has been growing faster than the ability of designers to produce hardware designs. Hence, there has been a growing interest in developing techniques and tools that facilitate the process of logic design.
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is motivated by following observation: the more attributes some people share, the more difficult to separate their identities. Based on this observation, we develop HPIM via a transferred attribute describer, a deep multi-attribute classifier trained from the source noisy person attribute datasets. We encode each image into the attribute probabilistic description in the target person re-ID dataset. Afterwards in the attribute code space, we consider each person as a distribution to generate his view-specific attribute codes in different practical scenarios. Hence we estimate the person-specific statistical moments from zeroth to higher order, which are further used to calculate the central moment discrepancies between persons. Such discrepancy is a ground to choose hard identity to organize proper mini-batches, without concerning the person representation changing in metric learning. It presents as a complementary tool of hard example mining, which helps to explore the global instead of the local hard example constraint in the mini-batch built by randomly sampled identities. Extensive experiments on two person re-identification benchmarks validated the effectiveness of our proposed algorithm.
Are commercially implemented adaptive cruise control systems string stable?
Venue: arXiv cs.SY 2019
Year: 2019

In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles.