OpenRaiser/Intern-Atlas huggingface dataset:
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithms
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Venue: arXiv cs.LG 2018
Year: 2018

We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms that: (1) subsample the dataset, and then (2) applies a randomized mechanism M to the subsample, in terms of the RDP parameters of M and the subsampling probability parameter. Our results generalize the moments accounting technique, developed by Abadi et al. (2016) for the Gaussian mechanism, to any subsampled RDP mechanism.
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the three-dimensional (3D) aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an offline resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service (QoS) requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on online algorithm design which only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the offline scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on successive convex approximation. Our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descents again to a lower altitude to reduce the path loss of the communication links to the users it serves.
Sourcerer's Apprentice and the study of code snippet migration
Venue: arXiv cs.SE 2018
Year: 2018

On the worldwide web, not only are webpages connected but source code is too. Software development is becoming more accessible to everyone and the licensing for software remains complicated. We need to know if software licenses are being maintained properly throughout their reuse and evolution. This motivated the development of the Sourcerer's Apprentice, a webservice that helps track clone relicensing, because software typically employ software licenses to describe how their software may be used and adapted. But most developers do not have the legal expertise to sort out license conflicts. In this paper we put the Apprentice to work on empirical studies that demonstrate there is much sharing between StackOverflow code and Python modules and Python documentation that violates the licensing of the original Python modules and documentation: software snippets shared through StackOverflow are often being relicensed improperly to CC-BY-SA 3.0 without maintaining the appropriate attribution. We show that many snippets on StackOverflow are inappropriately relicensed by StackOverflow users, jeopardizing the status of the software built by companies and developers who reuse StackOverflow snippets.
CRUM: Checkpoint-Restart Support for CUDA's Unified Memory
Venue: arXiv cs.DC 2018
Year: 2018

Unified Virtual Memory (UVM) was recently introduced on recent NVIDIA GPUs. Through software and hardware support, UVM provides a coherent shared memory across the entire heterogeneous node, migrating data as appropriate. The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments. Newer CUDA programs have started taking advantage of UVM for the same reasons of superior programmability that UNIX applications long ago switched to assuming the presence of virtual memory. Therefore, checkpointing of UVM will become increasingly important, especially as NVIDIA CUDA continues to gain wider popularity: 87 of the top 500 supercomputers in the latest listings are GPU-accelerated, with a current trend of ten additional GPU-based supercomputers each year. A new scalable checkpointing mechanism, CRUM (Checkpoint-Restart for Unified Memory), is demonstrated for hybrid CUDA/MPI computations across multiple computer nodes. CRUM supports a fast, forked checkpointing, which mostly overlaps the CUDA computation with storage of the checkpoint image in stable storage. The runtime overhead of using CRUM is 6% on average, and the time for forked checkpointing is seen to be a factor of up to 40 times less than traditional, synchronous checkpointing.
Study of Polarization-Driven Shortening for Polar Codes Designed with the Gaussian Approximation
Venue: arXiv cs.IT 2018
Year: 2018

This paper presents a polarization-driven (PD) shortening technique for the design of rate-compatible polar codes. The proposed shortening strategy consists of reducing the generator matrix by relating its row index with the channel polarization index. We assume that the shortened bits are known by both the encoder and the decoder and employ successive cancellation (SC) for decoding the shortened codes constructed by the proposed PD technique. A performance analysis is then carried out based on the Spectrum Distance (SD). Simulations show that the proposed PD-based shortened polar codes outperform existing shortened polar codes.
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks
Venue: arXiv cs.LG 2018
Year: 2018

Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection has become more and more popular. In this paper, a new domain-based anomaly detection method based on generative adversarial networks (GAN) is proposed. Minimum likelihood regularization is proposed to make the generator produce more anomalies and prevent it from converging to normal data distribution. Proper ensemble of anomaly scores is shown to improve the stability of discriminator effectively. The proposed method has achieved significant improvement than other anomaly detection methods on Cifar10 and UCI datasets.
Experience, Imitation and Reflection; Confucius' Conjecture and Machine Learning
Venue: arXiv cs.AI 2018
Year: 2018

Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current artificial intelligence applications can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Having that in mind, and considering the several existing machine learning methods this question rises that 'What are some of the best ways for a machine to learn?' Regarding the learning methods of human, Confucius' point of view is that they are by experience, imitation and reflection. This paper tries to explore and discuss regarding these three ways of learning and their implementations in machines by having a look at how they happen in minds.
Using Standard Typing Algorithms Incrementally
Venue: arXiv cs.PL 2018
Year: 2018

Modern languages are equipped with static type checking/inference that helps programmers to keep a clean programming style and to reduce errors. However, the ever-growing size of programs and their continuous evolution require building fast and efficient analysers. A promising solution is incrementality, so one only re-types those parts of the program that are new, rather than the entire codebase. We propose an algorithmic schema driving the definition of an incremental typing algorithm that exploits the existing, standard ones with no changes. Ours is a grey-box approach, meaning that just the shape of the input, that of the results and some domain-specific knowledge are needed to instantiate our schema. Here, we present the foundations of our approach and we show it at work to derive three different incremental typing algorithms. The first two implement type checking and inference for a functional language. The last one type-checks an imperative language to detect information flow and non-interference. We assessed our proposal on a prototypical implementation of an incremental type checker. Our experiments show that using the type checker incrementally is (almost) always rewarding.
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining
Venue: arXiv cs.CV 2018
Year: 2018

A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that our model trained with this mined supervision generates visual groundings that achieve a higher correlation with respect to manually-annotated groundings, meanwhile achieving state-of-the-art VQA accuracy.
Non-Orthogonal Multiple Access for 5G and Beyond
Venue: arXiv cs.IT 2018
Year: 2018

Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultra-high-definition video, virtual reality etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Non-orthogonal multiple access (NOMA), which has been recently proposed for the 3rd generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the above-mentioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state-of-the-art in power-domain multiplexing aided NOMA, with a focus on the theoretical NOMA principles, multiple antenna aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the co-existence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.
Model-order selection in statistical shape models
Venue: arXiv cs.LG 2018
Year: 2018

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.
SEA: A Combined Model for Heat Demand Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.
Variational dynamic interpolation for kinematic systems on trivial principal bundles
Venue: arXiv cs.SY 2018
Year: 2018

This article presents the dynamic interpolation problem for locomotion systems evolving on a trivial principal bundle $Q$. Given an ordered set of points in $Q$, we wish to generate a trajectory which passes through these points by synthesizing suitable controls. The global product structure of the trivial bundle is used to obtain an induced Riemannian product metric on $Q$. The squared $L^2-$norm of the covariant acceleration is considered as the cost function, and its first order variations are taken for generating the trajectories. The nonholonomic constraint is enforced through the local form of the principal connection and the group symmetry is employed for reduction. The explicit form of the Riemannian connection for the trivial bundle is employed to arrive at the extremal of the cost function. The result is applied to generate a trajectory for the generalized Purcell's swimmer - a low Reynolds number microswimming mechanism.
Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters
Venue: arXiv cs.DC 2018
Year: 2018

Software container solutions have revolutionized application development approaches by enabling lightweight platform abstractions within the so-called "containers." Several solutions are being actively developed in attempts to bring the benefits of containers to high-performance computing systems with their stringent security demands on the one hand and fundamental resource sharing requirements on the other. In this paper, we discuss the benefits and short-comings of such solutions when deployed on real HPC systems and applied to production scientific applications.We highlight use cases that are either enabled by or significantly benefit from such solutions. We discuss the efforts by HPC system administrators and support staff to support users of these type of workloads on HPC systems not initially designed with these workloads in mind focusing on NCSA's Blue Waters system.
Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Weather conditions often disrupt the proper functioning of transportation systems. Present systems either deploy an array of sensors or use an in-vehicle camera to predict weather conditions. These solutions have resulted in incremental cost and limited scope. To ensure smooth operation of all transportation services in all-weather conditions, a reliable detection system is necessary to classify weather in wild. The challenges involved in solving this problem is that weather conditions are diverse in nature and there is an absence of discriminate features among various weather conditions. The existing works to solve this problem have been scene specific and have targeted classification of two categories of weather. In this paper, we have created a new open source dataset consisting of images depicting three classes of weather i.e rain, snow and fog called RFS Dataset. A novel algorithm has also been proposed which has used super pixel delimiting masks as a form of data augmentation, leading to reasonable results with respect to ten Convolutional Neural Network architectures.
Classification of Building Information Model (BIM) Structures with Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.
Two-pass Light Field Image Compression for Spatial Quality and Angular Consistency
Venue: arXiv cs.MM 2018
Year: 2018

The quality assessment of light field images presents new challenges to conventional compression methods, as the spatial quality is affected by the optical distortion of capturing devices, and the angular consistency affects the performance of dynamic rendering applications. In this paper, we propose a two-pass encoding system for pseudo-temporal sequence based light field image compression with a novel frame level bit allocation framework that optimizes spatial quality and angular consistency simultaneously. Frame level rate-distortion models are estimated during the first pass, and the second pass performs the actual encoding with optimized bit allocations given by a two-step convex programming. The proposed framework supports various encoder configurations. Experimental results show that comparing to the anchor HM 16.16 (HEVC reference software), the proposed two-pass encoding system on average achieves 11.2% to 11.9% BD-rate reductions for the all-intra configuration, 15.8% to 32.7% BD-rate reductions for the random-access configuration, and 12.1% to 15.7% BD-rate reductions for the low-delay configuration. The resulting bit errors are limited, and the total time cost is less than twice of the one-pass anchor. Comparing with our earlier low-delay configuration based method, the proposed system improves BD-rate reduction by 3.1% to 8.3%, reduces the bit errors by more than 60%, and achieves more than 12x speed up.
Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting
Venue: arXiv cs.CL 2018
Year: 2018

Speech recognition is a sequence prediction problem. Besides employing various deep learning approaches for framelevel classification, sequence-level discriminative training has been proved to be indispensable to achieve the state-of-the-art performance in large vocabulary continuous speech recognition (LVCSR). However, keyword spotting (KWS), as one of the most common speech recognition tasks, almost only benefits from frame-level deep learning due to the difficulty of getting competing sequence hypotheses. The few studies on sequence discriminative training for KWS are limited for fixed vocabulary or LVCSR based methods and have not been compared to the state-of-the-art deep learning based KWS approaches. In this paper, a sequence discriminative training framework is proposed for both fixed vocabulary and unrestricted acoustic KWS. Sequence discriminative training for both sequence-level generative and discriminative models are systematically investigated. By introducing word-independent phone lattices or non-keyword blank symbols to construct competing hypotheses, feasible and efficient sequence discriminative training approaches are proposed for acoustic KWS. Experiments showed that the proposed approaches obtained consistent and significant improvement in both fixed vocabulary and unrestricted KWS tasks, compared to previous frame-level deep learning based acoustic KWS methods.