Miracle-dz/UniFAR-FTUs huggingface dataset:
Composition-aided Sketch-realistic Portrait Generation

Sketch portrait generation is of wide applications including digital entertainment and law enforcement. Despite the great progress achieved by existing face sketch generation methods, they mostly yield blurred effects and great deformation over various facial parts. In order to tackle this challenge, we propose a novel composition-aided generative adversarial network (CA-GAN) for sketch portrait generation. First, we utilize paired inputs including a face photo and the corresponding pixel-wise face labels for generating the portrait. Second, we propose an improved pixel loss, termed compositional loss, to focus training on hard-generated components and delicate facial structures. Moreover, we use stacked CA-GANs (stack-CA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating identity-preserving, sketch-realistic, and visually comfortable sketch portraits over a wide range of challenging data, and outperforms existing methods. Besides, our methods show considerable generalization ability.
OMR: out-of-core MapReduce for large data sets

While single machine MapReduce systems can squeeze out maximum performance from available multi-cores, they are often limited by the size of main memory and can thus only process small datasets. Our experience shows that the state-of-the-art single-machine in-memory MapReduce system Metis frequently experiences out-of-memory crashes. Even though today's computers are equipped with efficient secondary storage devices, the frameworks do not utilize these devices mainly because disk access latencies are much higher than those for main memory. Therefore, the single-machine setup of the Hadoop system performs much slower when it is presented with the datasets which are larger than the main memory. Moreover, such frameworks also require tuning a lot of parameters which puts an added burden on the programmer. In this paper we present OMR, an Out-of-core MapReduce system that not only successfully handles datasets that are far larger than the size of main memory, it also guarantees linear scaling with the growing data sizes. OMR actively minimizes the amount of data to be read/written to/from disk via on-the-fly aggregation and it uses block sequential disk read/write operations whenever disk accesses become necessary to avoid running out of memory. We theoretically prove OMR's linear scalability and empirically demonstrate it by processing datasets that are up to 5x larger than main memory. Our experiments show that in comparison to the standalone single-machine setup of the Hadoop system, OMR delivers far higher performance. Also in contrast to Metis, OMR avoids out-of-memory crashes for large datasets as well as delivers higher performance when datasets are small enough to fit in main memory.
Bidirectional deep echo state networks

In this work we propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embeds the temporal relationships in the data. To overcome the limitations of the reservoir vanishing memory, we introduce a bidirectional reservoir, whose last state captures also the past dependencies in the input. We apply dimensionality reduction to the final reservoir states to obtain compressed fixed size representations of the time series. These are subsequently fed into a deep feedforward network, which is trained to perform the final classification. We test our architecture on benchmark datasets and on a real-world use-case of blood samples classification. Results show that our method performs better than a standard echo state network, and it can be trained much faster than a fully-trained recurrent network.
IP Telephony: Deploying VoIP Protocols and IMS Infrastructure

All you need to know about deploying VoIP protocols in one comprehensive and highly practical reference - Now updated with coverage on SIP and the IMS infrastructureThis book provides a comprehensive and practical overview of the technology behind Internet Telephony (IP), providing essential information to Network Engineers, Designers, and Managers who need to understand the protocols. Furthermore, the author explores the issues involved in the migration of existing telephony infrastructure to an IP - based real time communication service. Assuming a working knowledge of IP and networking, it addresses the technical aspects of real-time applications over IP. Drawing on his extensive research and practical development experience in VoIP from its earliest stages, the author provides an accessible reference to all the relevant standards and cutting-edge techniques in a single resource.Key Features:Updated with a chapter on SIP and the IMS infrastructure Covers ALL the major VoIP protocols SIP, H323 and MGCP Includes a large section on practical deployment issues gleaned from the authors own experience Chapter on the rationale for IP telephony and description of the technical and business drivers for transitioning to all IP networks This book will be a valuable guide for professional network engineers, designers and managers, decision makers and project managers overseeing VoIP implementations, market analysts, and consultants. Advanced undergraduate and graduate students undertaking data/voice/multimedia communications courses will also find this book of interest.Olivier Hersent founded NetCentrex, a leading provider of VoIP infrastructure for service providers, then became CTO of Comverse after the acquisition of NetCentrex. He now manages Actility, provider of IMS based M2M and smartgrid infrastructure and applications.
AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder

Cache contention is an important type of performance anomaly in this multi-core and many-core era. It can cause a significant slowdown in parallel programs. However, it is hard to detect and often, not visible in the source code. As software changes over time, modifications in code can introduce cache contention anomalies. One way to detect such anomalies, is to use performance regression testing. Prior approaches for cache contention detection are either not suitable for performance regression testing or requires knowledge about specific type of contention behavior. To remedy these shortcomings, we propose AutoCon. It works by finding the modified functions and collecting hardware performance counter profiles for them. It uses an unsupervised learning technique, called Autoencoder, to learn the contention behavior implied by the profiles (collected from the older version of code). Then, it checks the profiles collected from the newer version of code to determine whether the contention pattern (implied by the profiles) is anomalous. If so, AutoCon reports a cache contention anomaly. Finally, it performs root cause analysis to provide detailed debugging information. AutoCon is the first learning based cache contention detector that does not require any positive example of contention anomalies. We evaluated AutoCon with 13 real world cache contention anomalies as well as 7 open source programs. AutoCon detected all types of cache contention anomalies with only 3.7% profiling overhead (on average). Moreover, compared to a state-of-the-art cache contention detector, AutoCon detected more anomalies.
Self-Supervised Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we train a deep network, using fully automatic supervision, to predict relative scene depth from single images. The depth training images are automatically derived from simple videos of cars moving through a scene, using classic depth from motion techniques, and no human provided labels. We show that this pretext task of predicting depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. In particular, our pre-trained model outperforms an ImageNet counterpart for the monocular depth estimation task. Unlike work that analyzes video paired with additional information about direction of motion, our agent learns from "raw egomotion" video recorded from cars moving through the world. Unlike methods that require videos of moving objects, we neither depend on, nor are disrupted by, moving objects in the video. Indeed, we can benefit from predicting depth in the videos associated with various downstream tasks, showing that we can adapt to new scenes in an unsupervised manner to improve performance. By doing so, we achieve consistently better results over several different urban scene understanding tasks, obtaining results that are competitive with state-of-the-art method for monocular depth estimation.
Composite Right/Left-Handed Transmission Lines Based on Conductor-Backed Coplanar Strips

We propose here a composite right/left handed transmission line constructed by using conductor-backed coplanar strips. In this line, we can easily realize a shunt inductor without via because it has the electric-wall symmetry at the guide center. The left-handed nature is verified by both the finite difference time-domain (FDTD) and the equivalent-circuit calculations. Furthermore, we demonstrate the proposed line can easily satisfy the balanced condition for no band gap between the right-handed and the left-handed modes, and can be applied to a leaky-wave antenna, numerically and experimentally.
Representing Videos based on Scene Layouts for Recognizing Agent-in-Place Actions

We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places in a specific scene layout. LIVR partitions the semantic features of a video clip into different places to force the network to learn place-based feature descriptions; to predict the confidence of each action, LIVR aggregates features from the place associated with an action and its adjacent places on the scene layout. We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes.
Evolving Latent Space Model for Dynamic Networks

Networks observed in the real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real-world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches. To the best of our knowledge, this is the first work that integrates statistical modeling of dynamic networks with deep learning for community detection and link prediction.
The DCA:SOMe Comparison A comparative study between two biologically-inspired algorithms

The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a 'context aware' detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a Self-Organizing Map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.
A Real-time Action Prediction Framework by Encoding Temporal Evolution for Assembly Tasks

Anticipating future actions is a key component of intelligence, specifically when it applies to real-time systems, such as robots or autonomous cars. While recent work has addressed prediction of raw RGB pixel values in future video frames, we focus on predicting further in future by predicting a summary of moving pixels through a sequence of frames which we call dynamic images. More precisely, given a dynamic image, we predict the motion evolution through next unseen video frames. Since this representation consists of a sequence of frames, we can go one second further into the future compared to the previous work in this field. We employed convolutional LSTMs to train our network on the dynamic images in an unsupervised learning process. Since our final goal is predicting the next action of a complex task such as an assembly task, we exploited labelled actions for the recognition process on top of predicted dynamic images. We show the effectiveness of our method on predicting the next human action in the above-mentioned task through the two-step process of predicting the next dynamic image and recognizing the action which it represents.
Coding together in a social network: collaboration among GitHub users

In this article we investigate developers involved in the creation of Open Source software to identify which characteristics favor innovation in the Open Source community. The results of the analysis show that higher reputation in the community improves the probability of gaining collaborators to a certain degree, but developers are also driven by reciprocity. This is consistent with the concept of gift economy. A significant network effect exists and emerges from standardization, showing that developers using the most popular programming languages in the service are likely to have more collaborators. Providing additional information (valid URL to developer's homepage) improves the chances of finding coworkers. The results can be generalized for the population of mature users of GitHub.
Value-Aware Item Weighting for Long-Tail Recommendation

Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to improve long-tail recommendation. Our approach works as a simple yet powerful add-on to existing recommendation algorithms for making a tunable trade-off between accuracy and long-tail coverage.
Extreme 3D Face Reconstruction: Looking Past Occlusions

Existing single view, 3D face reconstruction methods can produce beautifully detailed 3D results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide detailed 3D reconstructions of faces viewed under extreme conditions, out of plane rotations, and occlusions. Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which acts as a foundation and then separately layer this foundation with details represented by a bump map. We show how a deep convolutional encoder-decoder can be used to estimate such bump maps. We further show how this approach naturally extends to generate plausible details for occluded facial regions. We test our approach and its components extensively, quantitatively demonstrating the invariance of our estimated facial details. We further provide numerous qualitative examples showing that our method produces detailed 3D face shapes in viewing conditions where existing state of the art often break down.
Structure-Aware and Temporally Coherent 3D Human Pose Estimation

Deep learning methods for 3D human pose estimation from RGB images require a huge amount of domain-specific labeled data for good in-the-wild performance. However, obtaining annotated 3D pose data requires a complex motion capture setup which is generally limited to controlled settings. We propose a semi-supervised learning method using a structure-aware loss function which is able to utilize abundant 2D data to learn 3D information. Furthermore, we present a simple temporal network which uses additional context present in pose sequences to improve and temporally harmonize the pose estimates. Our complete pipeline improves upon the state-of-the-art by 11.8% and works at 30 FPS on a commodity graphics card.
Predicting Component Failures at Early Design Time

For the effective prevention and elimination of defects and failures in a software system, it is important to know which parts of the software are more likely to contain errors, and therefore, can be considered as “risky”. To increase reliability and quality, more effort should be spent in risky components during design, implementation, and testing. Examining the version archive and the code of a large open-source project, we have investigated the relation between the risk of components as measured by post-release failures, and different code structures; such as method calls, variables, exception handling expressions and inheritance statements. We have analyzed the different types of usage relations between components, and their effects on the failures. We utilized three commonly used statistical techniques to build failure prediction models. As a realistic opponent to our models, we introduced a “simple prediction model” which makes use of the riskiness information from the available components, rather than making random guesses. While the results from the classification experiments supported the use of code structures to predict failure-proneness, our regression analyses showed that the design time decisions also affected component riskiness. Our models were able to make precise predictions, with even only the knowledge of the inheritance relations. Since inheritance relations are defined earliest at design time; based on the results of this study, we can say that it may be possible to initialize preventive actions against failures even early in the design phase of a project.
A Shannon's theory of knowledge

Our so-called Information Society, is gradually changing into a Knowledge Society. The Shannons Theory provided a formal and mathematical framework for information. It was very fruitful for avoiding ambiguity on the concept of information. This paper proposes a transposition of this theory for knowledge. From the three axes of a formal model designed for knowledge engineering (information, sense and context), three quantitative measures are proposed to get a measure of the quantity of knowledge of a system. This notion permits to consider applications as the cognitive measure of a web site, a knowledge community (community of practice )
Graph Embedding with Rich Information through Bipartite Heterogeneous Network

Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding only rely on the typology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network. Specially, we designed a biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. The efficacy of our method is demonstrated by extensive comparison experiments with several baselines on various datasets. It improves the Micro-F1 and Macro-F1 of node classification by 10% and 7% on Cora dataset.
Temporal and Volumetric Denoising via Quantile Sparse Image (QuaSI) Prior in Optical Coherence Tomography and Beyond

This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical image modalities. We demonstrate its effectivness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show differnt noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is fascilitated through use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple dataset emphasize the excellent performance of the proposed method.
Classifying and visualizing students' cognitive engagement in course readings

Reading material has been part of course teaching for centuries, but until recently students' engagement with that reading, and its effect on their learning, has been difficult for teachers to assess. In this article, we explore the idea of examining cognitive engagement---a measure of how deeply a student is thinking about course material, which has been shown to correlate with learning gains---as it varies over different sections of the course reading material. We show that a combination of automatic classification and visualization of cognitive engagement anchored in the text can give teachers---and not only researchers---valuable insight into their students' thinking, suggesting ways to modify their lectures and their course readings to improve learning. We demonstrate this approach with analyzing students' comments in two different courses (Physics and Biology) using the Nota Bene annotation platform.
Probabilistic Knowledge Transfer for Deep Representation Learning

Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT methods are tailored towards classification tasks and they cannot be used efficiently for other representation learning tasks. In this paper a novel knowledge transfer technique, that is capable of training a student model that maintains the same amount of mutual information between the learned representation and a set of (possible unknown) labels as the teacher model, is proposed. Apart from outperforming existing KT techniques, the proposed method allows for overcoming several limitations of existing methods providing new insight into KT as well as novel KT applications, ranging from knowledge transfer from handcrafted feature extractors to {cross-modal} KT from the textual modality into the representation extracted from the visual modality of the data.