OpenRaiser/Intern-Atlas huggingface dataset:
DCT-domain Deep Convolutional Neural Networks for Multiple JPEG Compression Classification
Venue: arXiv cs.MM 2017
Year: 2017

With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous. This ease of digital image capture and sharing is also accompanied by widespread usage of user-friendly image editing software. Thus, we are in an era where digital images can be very easily used for the massive spread of false information and their integrity need to be seriously questioned. Application of multiple lossy compressions on images is an essential part of any image editing pipeline involving lossy compressed images. This paper aims to address the problem of classifying images based on the number of JPEG compressions they have undergone, by utilizing deep convolutional neural networks in DCT domain. The proposed system incorporates a well designed pre-processing step before feeding the image data to CNN to capture essential characteristics of compression artifacts and make the system image content independent. Detailed experiments are performed to optimize different aspects of the system, such as depth of CNN, number of DCT frequencies, and execution time. Results on the standard UCID dataset demonstrate that the proposed system outperforms existing systems for multiple JPEG compression detection and is capable of classifying more number of re-compression cycles then existing systems.
Cleverarm: A Novel Exoskeleton For Rehabilitation Of Upper Limb Impairments
Venue: arXiv cs.RO 2017
Year: 2017

CLEVERarm (Compact, Low-weight, Ergonomic, Virtual and Augmented Reality Enhanced Rehabilitation arm) is a novel exoskeleton with eight degrees of freedom supporting the motion of shoulder girdle, glenohumeral joint, elbow and wrist. Of the eight degrees of freedom of the exoskeleton, six are active and the two degrees of freedom supporting the motion of wrist are passive. This paper briefly outlines the design of CLEVERarm and its control architectures.
High performance ultra-low-precision convolutions on mobile devices
Venue: arXiv cs.LG 2017
Year: 2017

Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power. This is particularly true for workloads running on older consumer phones, where a typical device might be powered by a single- or dual-core ARMv7 CPU. We provide an open-source implementation and a comprehensive analysis of (to our knowledge) the state of the art ultra-low-precision (<4 bit precision) implementation of the core primitives required for modern deep learning workloads on ARMv7 devices, and demonstrate speedups of 4x-20x over our additional state-of-the-art float32 and int8 baselines.
Towards Methods for Model-Based Software Development
Venue: arXiv cs.SE 2017
Year: 2017

Software engineering is a young discipline. Despite efforts in recent years, some elements still require further development, research, and systematization. One of these elements are methods. They consist of a set of well-defined activities used in a software development to guide how the work should be performed to achieve the expected results. However, there is a lack of systematic knowledge that effectively guide how work should be done in a variety of areas. Despite the knowledge available in Software Engineering Body of Knowledge (SWEBOK), only the classical methods are comprised there, while emerging topics such as Agent-Oriented Software Engineering (AOSE), Aspect-Oriented Software Development (AOSD), and Model-Based Software Development (MBSE) are only briefly mentioned. We claim that it is also necessary to investigate methods for those emerging software engineering subtracks. In this direction, this paper presents a a speculative and preliminary emerging results on the establishment of methods for MBSE. We report some insights on the conception of methods for MBSE.
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN.
A general cipher for individual data anonymization
Venue: arXiv cs.CR 2017
Year: 2017

Over the years, the literature on individual data anonymization has burgeoned in many directions. Borrowing from several areas of other sciences, the current diversity of concepts, models and tools available contributes to understanding and fostering individual data dissemination in a privacy-preserving way, as well as unleashing new sources of information for the benefits of society at large. However, such diversity doesn't come without some difficulties. Currently, the task of selecting the optimal analytical environment to conduct anonymization is complicated by the multitude of available choices. Based on recent contributions from the literature and inspired by cryptography, this paper proposes the first cipher for data anonymization. The functioning of this cipher shows that, in fact, every anonymization method can be viewed as a general form of rank swapping with unconstrained permutation structures. Beyond all the currently existing methods that it can mimic, this cipher offers a new way to practice data anonymization, notably by performing anonymization in an ex ante way, instead of being engaged in several ex post evaluations and iterations to reach the protection and information properties sought after. Moreover, the properties of this cipher point to some previously unknown general insights into the task of data anonymization considered at a general level of functioning. Finally, and to make the cipher operational, this paper proposes the introduction of permutation menus in data anonymization, where recently developed universal measures of disclosure risk and information loss are used ex ante for the calibration of permutation keys. To justify the relevance of their uses, a theoretical characterization of these measures is also proposed.
Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Venue: arXiv cs.CL 2017
Year: 2017

$\textbf{Objective}$ Develop an automatic diagnostic system which only uses textual admission information from Electronic Health Records (EHRs) and assist clinicians with a timely and statistically proved decision tool. The hope is that the tool can be used to reduce mis-diagnosis. $\textbf{Materials and Methods}$ We use the real-world clinical notes from MIMIC-III, a freely available dataset consisting of clinical data of more than forty thousand patients who stayed in intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional Neural Network model to learn semantic features from unstructured textual input and automatically predict primary discharge diagnosis. $\textbf{Results}$ The proposed model achieved an overall 96.11% accuracy and 80.48% weighted F1 score values on 10 most frequent disease classes, significantly outperforming four strong baseline models by at least 12.7% in weighted F1 score. $\textbf{Discussion}$ Experimental results imply that the CNN model is suitable for supporting diagnosis decision making in the presence of complex, noisy and unstructured clinical data while at the same time using fewer layers and parameters that other traditional Deep Network models. $\textbf{Conclusion}$ Our model demonstrated capability of representing complex medical meaningful features from unstructured clinical notes and prediction power for commonly misdiagnosed frequent diseases. It can use easily adopted in clinical setting to provide timely and statistically proved decision support. $\textbf{Keywords}$ Convolutional neural network, text classification, discharge diagnosis prediction, admission information from EHRs.
Hybrid eye center localization using cascaded regression and hand-crafted model fitting
Venue: arXiv cs.CV 2017
Year: 2017

We propose a new cascaded regressor for eye center detection. Previous methods start from a face or an eye detector and use either advanced features or powerful regressors for eye center localization, but not both. Instead, we detect the eyes more accurately using an existing facial feature alignment method. We improve the robustness of localization by using both advanced features and powerful regression machinery. Unlike most other methods that do not refine the regression results, we make the localization more accurate by adding a robust circle fitting post-processing step. Finally, using a simple hand-crafted method for eye center localization, we show how to train the cascaded regressor without the need for manually annotated training data. We evaluate our new approach and show that it achieves state-of-the-art performance on the BioID, GI4E, and the TalkingFace datasets. At an average normalized error of e < 0.05, the regressor trained on manually annotated data yields an accuracy of 95.07% (BioID), 99.27% (GI4E), and 95.68% (TalkingFace). The automatically trained regressor is nearly as good, yielding an accuracy of 93.9% (BioID), 99.27% (GI4E), and 95.46% (TalkingFace).
Stochastic reconstruction of an oolitic limestone by generative adversarial networks
Venue: arXiv cs.CV 2017
Year: 2017

Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stochastic image reconstruction based on generative adversarial neural networks (GANs). GANs represent a framework of unsupervised learning methods that require no a priori inference of the probability distribution associated with the training data. Using a fully convolutional neural network allows fast sampling of large volumetric images.We apply a GAN based workflow of network training and image generation to an oolitic Ketton limestone micro-CT dataset. Minkowski functionals, effective permeability as well as velocity distributions of simulated flow within the acquired images are compared with the synthetic reconstructions generated by the deep neural network. While our results show that GANs allow a fast and accurate reconstruction of the evaluated image dataset, we address a number of open questions and challenges involved in the evaluation of generative network-based methods.
Per-Pixel Feedback for improving Semantic Segmentation
Venue: arXiv cs.LG 2017
Year: 2017

Semantic segmentation is the task of assigning a label to each pixel in the image.In recent years, deep convolutional neural networks have been driving advances in multiple tasks related to cognition. Although, DCNNs have resulted in unprecedented visual recognition performances, they offer little transparency. To understand how DCNN based models work at the task of semantic segmentation, we try to analyze the DCNN models in semantic segmentation. We try to find the importance of global image information for labeling pixels. Based on the experiments on discriminative regions, and modeling of fixations, we propose a set of new training loss functions for fine-tuning DCNN based models. The proposed training regime has shown improvement in performance of DeepLab Large FOV(VGG-16) Segmentation model for PASCAL VOC 2012 dataset. However, further test remains to conclusively evaluate the benefits due to the proposed loss functions across models, and data-sets. Submitted in part fulfillment of the requirements for the degree of Integrated Masters of Science in Applied Mathematics. Update: Further Experiment showed minimal benefits. Code Available [here](https://github.com/BardOfCodes/Seg-Unravel).
Sharing Storage in a Smart Grid: A Coalitional Game Approach
Venue: arXiv cs.SY 2017
Year: 2017

Sharing economy is a transformative socio-economic phenomenon built around the idea of sharing underused resources and services, e.g. transportation and housing, thereby reducing costs and extracting value. Anticipating continued reduction in the cost of electricity storage, we look into the potential opportunity in electrical power system where consumers share storage with each other. We consider two different scenarios. In the first scenario, consumers are assumed to already have individual storage devices and they explore cooperation to minimize the realized electricity consumption cost. In the second scenario, a group of consumers is interested to invest in joint storage capacity and operate it cooperatively. The resulting system problems are modeled using cooperative game theory. In both cases, the cooperative games are shown to have non-empty cores and we develop efficient cost allocations in the core with analytical expressions. Thus, sharing of storage in cooperative manner is shown to be very effective for the electric power system.
SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics
Venue: arXiv cs.RO 2017
Year: 2017

To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm x 20cm x 10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of +/- 1.27 cm in translation along x, y, and z directions and has motion modes for sinusoidal motion and breathing-inspired motion. Modular platform mounts were also designed for pattern cutting and debridement experiments. The platform's positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. All the details, CAD models, and control software for the platform is available at github.com/BerkeleyAutomation/sprk.
Layered Non-Orthogonal Random Access with SIC and Transmit Diversity for Reliable Transmissions
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study a layered random access scheme based on non-orthogonal multiple access (NOMA) to improve the throughput of multichannel ALOHA. At a receiver, successive interference cancellation (SIC) is carried out across layers to remove the signals that are already decoded. A closed- form expression for the total throughput is derived under certain assumptions. It is shown that the transmission rates of layers can be optimized to maximize the total throughput and the proposed scheme can improve the throughput with multiple layers. Furthermore, it is shown that the optimal rates can be recursively found using multiple individual one-dimensional optimizations. We also modify the proposed layered random access scheme with contention resolution repetition diversity for reliable transmissions with a delay constraint. It is shown to be possible to have a low outage probability if the number of copies can be optimized, which is desirable for high reliability low latency communications.
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network. In the case that camera poses of input images are fixed or known, such as with surveillance and satellite cameras, the pixel correspondence between the images captured at different times can be known. Hence, it is possible to comparatively accurately detect scene changes between the images by modeling the appearance of the scene. On the other hand, in case of cameras mounted on a moving object, such as ground and aerial vehicles, we must consider the spatial correspondence between the images captured at different times. However, it can be difficult to accurately estimate the camera pose or 3D model of a scene, owing to the scene changes or lack of imagery. To solve this problem, we propose a change detection convolutional neural network utilizing dense optical flow between input images to improve the robustness to the difference between camera viewpoints. Our evaluation based on the panoramic change detection dataset shows that the proposed method outperforms state-of-the-art change detection algorithms.
Software Defined Applications in Cellular and Optical Networks
Venue: arXiv cs.NI 2017
Year: 2017

Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.
Periortree: An Extention of R-Tree for Periodic Boundary Conditions
Venue: arXiv cs.DS 2017
Year: 2017

Searching spatial data is an important operation for scientific simulations which are performed mostly with periodic boundary conditions. An R-Tree is a well known tree data structure used to contain spatial objects and it is capable of answering to spatial searching queries in an efficient way. In this paper, a novel method to construct an R-Tree considering periodic boundary conditions is presented. Unlike existing methods, the proposed method works without any kind of extra objects or queries. Moreover, because this method reduces the volume of bounding box for each node under the periodic boundary conditions, it is expected to increase the overall efficiency. While the extension of an R-Tree is presented in this work, this method is not only applicable to an R-Tree but also to other data structures that use axis-aligned bounding boxes with periodic boundary conditions. The implementation is available on GitHub.
Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Venue: arXiv cs.LG 2017
Year: 2017

Spectral Clustering (SC) is a widely used data clustering method which first learns a low-dimensional embedding $U$ of data by computing the eigenvectors of the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get the final clustering result. The Sparse Spectral Clustering (SSC) method extends SC with a sparse regularization on $UU^\top$ by using the block diagonal structure prior of $UU^\top$ in the ideal case. However, encouraging $UU^\top$ to be sparse leads to a heavily nonconvex problem which is challenging to solve and the work (Lu, Yan, and Lin 2016) proposes a convex relaxation in the pursuit of this aim indirectly. However, the convex relaxation generally leads to a loose approximation and the quality of the solution is not clear. This work instead considers to solve the nonconvex formulation of SSC which directly encourages $UU^\top$ to be sparse. We propose an efficient Alternating Direction Method of Multipliers (ADMM) to solve the nonconvex SSC and provide the convergence guarantee. In particular, we prove that the sequences generated by ADMM always exist a limit point and any limit point is a stationary point. Our analysis does not impose any assumptions on the iterates and thus is practical. Our proposed ADMM for nonconvex problems allows the stepsize to be increasing but upper bounded, and this makes it very efficient in practice. Experimental analysis on several real data sets verifies the effectiveness of our method.
Computer Interfaces to Organizations: Perspectives on Borg-Human Interaction Design
Venue: arXiv cs.HC 2017
Year: 2017

We use the term borg to refer to the complex organizations composed of people, machines, and processes with which users frequently interact using computer interfaces and websites. Unlike interfaces to pure machines, we contend that borg-human interaction (BHI) happens in a context combining the anthropomorphization of the interface, conflict with users, and dramatization of the interaction process. We believe this context requires designers to construct the human facet of the borg, a structure encompassing the borg's personality, social behavior, and embodied actions; and the strategies to co-create dramatic narratives with the user. To design the human facet of a borg, different concepts and models are explored and discussed, borrowing ideas from psychology, sociology, and arts. Based on those foundations, we propose six design methodologies to complement traditional computer-human interface design techniques, including play-and-freeze enactment of conflicts and the use of giant puppets as interface prototypes.
A Frequency Domain Neural Network for Fast Image Super-resolution
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a frequency domain neural network for image super-resolution. The network employs the convolution theorem so as to cast convolutions in the spatial domain as products in the frequency domain. Moreover, the non-linearity in deep nets, often achieved by a rectifier unit, is here cast as a convolution in the frequency domain. This not only yields a network which is very computationally efficient at testing but also one whose parameters can all be learnt accordingly. The network can be trained using back propagation and is devoid of complex numbers due to the use of the Hartley transform as an alternative to the Fourier transform. Moreover, the network is potentially applicable to other problems elsewhere in computer vision and image processing which are often cast in the frequency domain. We show results on super-resolution and compare against alternatives elsewhere in the literature. In our experiments, our network is one to two orders of magnitude faster than the alternatives with an imperceptible loss of performance.