OpenRaiser/Intern-Atlas huggingface dataset:
Human Languages in Source Code: Auto-Translation for Localized Instruction
Venue: arXiv cs.CL 2019
Year: 2019

Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.
Incompleteness Ex Machina
Venue: arXiv cs.LO 2019
Year: 2019

In this essay we'll prove Gödel's incompleteness theorems twice. First, we'll prove them the good old-fashioned way. Then we'll repeat the feat in the setting of computation. In the process we'll discover that Gödel's work, rightly viewed, needs to be split into two parts: the transport of computation into the arena of arithmetic on the one hand and the actual incompleteness theorems on the other. After we're done there will be cake.
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
Venue: arXiv cs.CV 2019
Year: 2019

It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutions Merging Network (DDCM-Net) to detect these roads in lidar images. The DDCM-Net can effectively recognize multi-scale and complex shaped roads with similar texture and colors, and also is shown to have superior performance over existing methods. To further improve its ability to accurately infer categories of roads, we propose the use of a joint-task learning strategy that utilizes two auxiliary output branches, i.e, multi-class classification and binary segmentation, joined with the main output of full-class segmentation. This pushes the network towards learning more robust representations that are expected to boost the ultimate performance of the main task. In addition, we introduce an iterative-random-weighting method to automatically weigh the joint losses for auxiliary tasks. This can avoid the difficult and expensive process of tuning the weights of each task's loss by hand. The experiments demonstrate that our proposed joint-task DDCM-Net can achieve better performance with fewer parameters and higher computational efficiency than previous state-of-the-art approaches.
Approximating Vertex Cover using Structural Rounding
Venue: arXiv cs.DS 2019
Year: 2019

In this work, we provide the first practical evaluation of the structural rounding framework for approximation algorithms. Structural rounding works by first editing to a well-structured class, efficiently solving the edited instance, and "lifting" the partial solution to recover an approximation on the input. We focus on the well-studied Vertex Cover problem, and edit to the class of bipartite graphs (where Vertex Cover has an exact polynomial time algorithm). In addition to the naive lifting strategy for Vertex Cover described by Demaine et al., we introduce a suite of new lifting strategies and measure their effectiveness on a large corpus of synthetic graphs. We find that in this setting, structural rounding significantly outperforms standard 2-approximations. Further, simpler lifting strategies are extremely competitive with the more sophisticated approaches. The implementations are available as an open-source Python package, and all experiments are replicable.
Image Steganography: Protection of Digital Properties against Eavesdropping
Venue: arXiv cs.MM 2019
Year: 2019

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Different types of carrier file formats can be used, but digital images are the most popular ones because of their frequency on the internet. For hiding secret information in images, there exists a large variety of steganography techniques. Some are more complex than others and all of them have respective strong and weak points. Many applications may require absolute invisibility of the secret information. This paper intends to give an overview of image steganography, it's usage and techniques, basically, to store the confidential information within images such as details of working strategy, secret missions, criminal and confidential information in various organizations that work for the national security such as army, police, FBI, secret service etc. We develop a desktop application that incorporates Advanced Encryption Standard for encryption of the original message, and Spatially Desynchronized Steganography Algorithm for hiding the text file inside the image.
Disentangled Image Matting
Venue: arXiv cs.CV 2019
Year: 2019

Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap. In this paper, we argue that directly estimating the alpha matte from a coarse trimap is a major limitation of previous methods, as this practice tries to address two difficult and inherently different problems at the same time: identifying true blending pixels inside the trimap region, and estimate accurate alpha values for them. We propose AdaMatting, a new end-to-end matting framework that disentangles this problem into two sub-tasks: trimap adaptation and alpha estimation. Trimap adaptation is a pixel-wise classification problem that infers the global structure of the input image by identifying definite foreground, background, and semi-transparent image regions. Alpha estimation is a regression problem that calculates the opacity value of each blended pixel. Our method separately handles these two sub-tasks within a single deep convolutional neural network (CNN). Extensive experiments show that AdaMatting has additional structure awareness and trimap fault-tolerance. Our method achieves the state-of-the-art performance on Adobe Composition-1k dataset both qualitatively and quantitatively. It is also the current best-performing method on the alphamatting.com online evaluation for all commonly-used metrics.
Generating High Quality Random Numbers: A High Throughput Parallel Bitsliced Approach
Venue: arXiv cs.CR 2019
Year: 2019

In this work, by employing a bitsliced data representation as building blocks of algorithms, we showcase the capability and scalability of our proposed method in a variety of PRNG methods in the category of block and stream ciphers. While demonstrating the suitability of stream-ciphers for high throughput PRNG, as an example, we implement and investigate a bitsliced MICKEY 2.0 PRNG by altering the paradigm of internal functions and data structure. The LFSR-based (Linear Feedback Shift Register) nature of the PRNG in our implementation perfectly suits the GPU's many-core structure due to its register oriented architecture and allows the usage of bit slicing technique to further improve the performance. In our SIMD vectorized fully parallel GPU implementation, each GPU thread is capable of generating a remarkable number of 32 pseudo-random bits in each LFSR clock cycle. We then compare our implementation with some of the most significant PRNGs that display a satisfactory performance in both throughput and randomness criteria. The proposed implementation successfully passes the NIST test for statistical randomness and bit-wise correlation criteria. To the best of authors' best knowledge, our method outperforms the current best implementations in the literature for computer-based PRNG and the optical solutions in terms of performance and performance per cost, while maintaining an acceptable measure of randomness. Our highest performance among all of the implemented CPRNGs with the proposed method is achieved by the MICKEY 2.0 algorithm which shows 1.9x improvement over the state of the art NVIDIA's proprietary high-performance PRNG, cuRAND library, achieving 1.6 Tb/s of throughput on the affordable NVIDIA GTX 980 Ti.
Human-robot Collaborative Navigation Search using Social Reward Sources
Venue: arXiv cs.RO 2019
Year: 2019

This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.
Suggestions on Test Suite Improvements with Automatic Infection and Propagation Analysis
Venue: arXiv cs.SE 2019
Year: 2019

An extreme transformation removes the body of a method that is reached by one test case at least. If the test suite passes on the original program and still passes after the extreme transformation, the transformation is said to be undetected, and the test suite needs to be improved. In this work we propose a technique to automatically determine which of the following three reasons prevent the detection of the extreme transformation is : the test inputs are not sufficient to infect the state of the program; the infection does not propagate to the test cases; the test cases have a weak oracle that does not observe the infection. We have developed Reneri, a tool that observes the program under test and the test suite in order to determine runtime differences between test runs on the original and the transformed method. The observations gathered during the analysis are processed by Reneri to suggest possible improvements to the developers. We evaluate Reneri on 15 projects and a total of 312 undetected extreme transformations. The tool is able to generate a suggestion for each each undetected transformation. For 63% of the cases, the existing test cases can infect the program state, meaning that undetected transformations are mostly due to observability and weak oracle issues. Interviews with developers confirm the relevance of the suggested improvements and experiments with state of the art automatic test generation tools indicate that no tool can improve the existing test suites to fix all undetected transformations.
Dual-attention Focused Module for Weakly Supervised Object Localization
Venue: arXiv cs.CV 2019
Year: 2019

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.
Dynamic Fusion: Attentional Language Model for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Neural Machine Translation (NMT) can be used to generate fluent output. As such, language models have been investigated for incorporation with NMT. In prior investigations, two models have been used: a translation model and a language model. The translation model's predictions are weighted by the language model with a hand-crafted ratio in advance. However, these approaches fail to adopt the language model weighting with regard to the translation history. In another line of approach, language model prediction is incorporated into the translation model by jointly considering source and target information. However, this line of approach is limited because it largely ignores the adequacy of the translation output. Accordingly, this work employs two mechanisms, the translation model and the language model, with an attentive architecture to the language model as an auxiliary element of the translation model. Compared with previous work in English--Japanese machine translation using a language model, the experimental results obtained with the proposed Dynamic Fusion mechanism improve BLEU and Rank-based Intuitive Bilingual Evaluation Scores (RIBES) scores. Additionally, in the analyses of the attention and predictivity of the language model, the Dynamic Fusion mechanism allows predictive language modeling that conforms to the appropriate grammatical structure.
Polar Coding for the Wiretap Broadcast Channel with Multiple Messages
Venue: arXiv cs.IT 2019
Year: 2019

A polar coding scheme is proposed for the Wiretap Broadcast Channel with two legitimate receivers and one eavesdropper. We consider a model in which the transmitter wishes to send different confidential (and non-confidential) information to each legitimate receiver. First, we compare two inner-bounds on the achievable region of this model from the literature, and then we design a polar coding scheme that achieves the stronger one. In the proposed scheme, the encoding uses polar-based Marton's coding, where one inner-layer must be reliably decoded by both legitimate receivers, and each receiver must decode its own corresponding outer-layer. Due to the non-degradedness condition of the broadcast channel, the encoder builds a chaining construction that induces bidirectional dependencies between adjacent blocks. Indeed, these dependencies can occur between different encoding layers of different blocks. The use of two secret-keys, whose length are negligible in terms of rate, are required to prove that the polar code satisfies the strong secrecy condition.
Multi-Year Vector Dynamic Time Warping Based Crop Mapping
Venue: arXiv cs.LG 2019
Year: 2019

Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2016 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data. The VDTW method achieved 99.85% and 99.74% overall accuracies for the same and cross years, respectively with fewer training samples compared to other state-of-the-art approaches, i.e. spectral angle mapper (SAM), dynamic time warping (DTW), time-weighted DTW (TWDTW), random forest (RF), support vector machine (SVM) and deep long short-term memory (LSTM) methods. The proposed method could be expanded for other crop types and/or geographical areas.
Performance Estimation of Container-Based Cloud-to-Fog Offloading
Venue: arXiv cs.DC 2019
Year: 2019

Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The research in this paper is motivated towards characterising and estimating the time taken to offload a service using containers, which is investigated in the context of the `Save and Load' container migration technique. To this end, the research addresses questions such as whether fog offloading can be accurately modelled and which system and network related parameters influence offloading. These are addressed by exploring a catalogue of 21 different metrics both at the system and process levels that is used as input to four estimation techniques using collective model and individual models to predict the time taken for offloading. The study is pursued by collecting over 1.1 million data points and the preliminary results indicate that offloading can be modelled accurately.
How Old Are You? Face Age Translation with Identity Preservation Using GANs
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel framework to generate images of different age while preserving identity information, which is known as face aging. Different from most recent popular face aging networks utilizing Generative Adversarial Networks(GANs) application, our approach do not simply transfer a young face to an old one. Instead, we employ the edge map as intermediate representations, firstly edge maps of young faces are extracted, a CycleGAN-based network is adopted to transfer them into edge maps of old faces, then another pix2pixHD-based network is adopted to transfer the synthesized edge maps, concatenated with identity information, into old faces. In this way, our method can generate more realistic transfered images, simultaneously ensuring that face identity information be preserved well, and the apparent age of the generated image be accurately appropriate. Experimental results demonstrate that our method is feasible for face age translation.
Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System
Venue: arXiv cs.CL 2019
Year: 2019

This is the application document for the 2019 Amazon Alexa competition. We give an overall vision of our conversational experience, as well as a sample conversation that we would like our dialog system to achieve by the end of the competition. We believe personalization, knowledge, and self-play are important components towards better chatbots. These are further highlighted by our detailed system architecture proposal and novelty section. Finally, we describe how we would ensure an engaging experience, how this research would impact the field, and related work.
User-Controlled Privacy-Preserving User Profile Data Sharing based on Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

The tremendous technological advancement in the last few decades has brought many enterprises to collaborate in a better way while making intelligent decisions. The use of Information Technology tools in obtaining data of people's everyday life from various autonomous data sources allowing unrestricted access to user data has emerged as an important practical issue and has given rise to legal implications. Various innovative models for data sharing and management have privacy and centrality issues. To alleviate these limitations, we have incorporated blockchain in user modeling. In this paper, we constructed a decentralized data sharing architecture with MultiChain blockchain in the travel domain, which is also applicable to other similar domains including education, health, and sports. Businesses that operate in the tourism industries including travel and tour agencies, hotels and resorts, shopping malls are connected to the MultiChain and they share their user profile data via stream in the MultiChain. The paper presents the hotel booking service for an imaginary hotel as one of the enterprise nodes, which collects user profile data with proper validation and will allow users to decide which of their data to be shared thus ensuring user control over their data and the preservation of privacy. The data from the repository is converted into an open data format while sharing via stream in the blockchain so that other enterprise nodes, after receiving the data, can easily convert them and store into their own repositories. The paper presents an evaluation of the performance of the model by measuring the latency and memory consumption with three test scenarios that mostly affect the user experience. The node responded quickly in all of these cases.
Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
Venue: arXiv cs.RO 2019
Year: 2019

Motion planning problems often have many local minima. Those minima are important to visualize to let a user guide, prevent or predict motions. Towards this goal, we develop the motion planning explorer, an algorithm to let users interactively explore a tree of local-minima. Following ideas from Morse theory, we define local minima as paths invariant under minimization of a cost functional. The local-minima are grouped into a local-minima tree using lower-dimensional projections specified by a user. The user can then interactively explore the local-minima tree, thereby visualizing the problem structure and guide or prevent motions. We show the motion planning explorer to faithfully capture local minima in four realistic scenarios, both for holonomic and certain non-holonomic robots.
Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
Venue: arXiv cs.RO 2019
Year: 2019

To engender safe and efficient human-robot collaboration, it is critical to generate high-fidelity predictions of human behavior. The challenges in making accurate predictions lie in the stochasticity and heterogeneity in human behaviors. This paper introduces a method for human trajectory and intention prediction through a multi-task model that is adaptable across different human subjects. We develop a nonlinear recursive least square parameter adaptation algorithm (NRLS-PAA) to achieve online adaptation. The effectiveness and flexibility of the proposed method has been validated in experiments. In particular, online adaptation can reduce the trajectory prediction error by more than 28% for a new human subject. The proposed human prediction method has high flexibility, data efficiency, and generalizability, which can support fast integration of HRC systems for user-specified tasks.
Bound on Peak-to-Average Power Ratio with Moment and Reduction Method
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we show bounds on Peak-to-Average Power Ratio (PAPR) and propose a method to reduce PAPR. These bounds are written in terms of moments of transmitted symbols. Further, we show some bounds in special cases and these special bounds include a practical case. From the practical bound, a method to reduce PAPR is derived. Our method is an extension of existing methods, a Partial Transmit Sequence (PTS) technique and a Selective Mapping (SLM) technique. Finally, we numerically verify performance of our method and discuss some conditions for our method.