OpenRaiser/Intern-Atlas huggingface dataset:
Talent Flow Analytics in Online Professional Network
Venue: arXiv cs.SI 2018
Year: 2018

Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly-accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework re-purposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that existence duration of the job (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform connectivity analysis at job and organization levels to derive insights on talent flow as well as job and organizational competitiveness.
Partial Person Re-identification with Alignment and Hallucination
Venue: arXiv cs.CV 2018
Year: 2018

Partial person re-identification involves matching pedestrian frames where only a part of a body is visible in corresponding images. This reflects practical CCTV surveillance scenario, where full person views are often not available. Missing body parts make the comparison very challenging due to significant misalignment and varying scale of the views. We propose Partial Matching Net (PMN) that detects body joints, aligns partial views and hallucinates the missing parts based on the information present in the frame and a learned model of a person. The aligned and reconstructed views are then combined into a joint representation and used for matching images. We evaluate our approach and compare to other methods on three different datasets, demonstrating significant improvements.
Supercompiling String Programs Using Word Equations as Constraints
Venue: arXiv cs.LO 2018
Year: 2018

We describe a general parameterized scheme of program and constraint analyses allowing us to specify both the program specialization method known as Turchin's supercompilation and Hmelevskii's algorithm solving the quadratic word equations. The scheme is specified for both sorts of the analysis and works in a joint algorithm in which these two sorts of the analysis are used together. The word equations and the inequalities on regular patterns are used as the string constraint language in the algorithm.
A Hybrid of Deep Audio Feature and i-vector for Artist Recognition
Venue: arXiv cs.SD 2018
Year: 2018

Artist recognition is a task of modeling the artist's musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative model i-vector and the discriminative model deep convolutional neural network. We show that this approach achieves state-of-the-art performance by complementing each other. In addition, we briefly explain the advantages and disadvantages of each approach.
Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks
Venue: arXiv cs.LG 2018
Year: 2018

We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN). Consisting of a perception module that extracts representations of latent object properties and a prediction module that uses those extracted properties to simulate system dynamics, the PPN can be trained in an end-to-end fashion purely from samples of object dynamics. The representations of latent object properties learned by PPNs not only are sufficient to accurately simulate the dynamics of systems comprised of previously unseen objects, but also can be translated directly into human-interpretable properties (e.g., mass, coefficient of restitution) in an entirely unsupervised manner. Crucially, PPNs also generalize to novel scenarios: their gradient-based training can be applied to many dynamical systems and their graph-based structure functions over systems comprised of different numbers of objects. Our results demonstrate the efficacy of graph-based neural architectures in object-centric inference and prediction tasks, and our model has the potential to discover relevant object properties in systems that are not yet well understood.
Iterative Amortized Inference
Venue: arXiv cs.LG 2018
Year: 2018

Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computationally efficient. However, standard inference models are restricted to direct mappings from data to approximate posterior estimates. The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap. We aim toward closing this gap by proposing iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients. Our approach generalizes standard inference models in VAEs and provides insight into several empirical findings, including top-down inference techniques. We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text.
Graph Compact Orthogonal Layout Algorithm
Venue: arXiv cs.OH 2018
Year: 2018

There exist many orthogonal graph drawing algorithms that minimize edge crossings or edge bends, however they produce unsatisfactory drawings in many practical cases. In this paper we present a grid-based algorithm for drawing orthogonal graphs with nodes of prescribed size. It distinguishes by creating pleasant and compact drawings in relatively small running time. The main idea is to minimize the total edge length that implicitly minimizes crossings and makes the drawing easy to comprehend. The algorithm is based on combining local and global improvements. Local improvements are moving each node to a new place and swapping of nodes. Global improvement is based on constrained quadratic programming approach that minimizes the total edge length while keeping node relative positions.
Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction
Venue: arXiv cs.CV 2018
Year: 2018

Much research has examined models for prediction of semantic labels or instances including dense pixel-wise prediction. The problem of predicting salient objects or regions of an image has also been examined in a similar light. With that said, there is an apparent relationship between these two problem domains in that the composition of a scene and associated semantic categories is certain to play into what is deemed salient. In this paper, we explore the relationship between these two problem domains. This is carried out in constructing deep neural networks that perform both predictions together albeit with different configurations for flow of conceptual information related to each distinct problem. This is accompanied by a detailed analysis of object co-occurrences that shed light on dataset bias and semantic precedence specific to individual categories.
Pre-trainable Reservoir Computing with Recursive Neural Gas
Venue: arXiv cs.LG 2018
Year: 2018

Echo State Networks (ESN) are a class of Recurrent Neural Networks (RNN) that has gained substantial popularity due to their effectiveness, ease of use and potential for compact hardware implementation. An ESN contains the three network layers input, reservoir and readout where the reservoir is the truly recurrent network. The input and reservoir layers of an ESN are initialized at random and never trained afterwards and the training of the ESN is applied to the readout layer only. The alternative of Recursive Neural Gas (RNG) is one of the many proposals of fully-trainable reservoirs that can be found in the literature. Although some improvements in performance have been reported with RNG, to the best of authors' knowledge, no experimental comparative results are known with benchmarks for which ESN is known to yield excellent results. This work describes an accurate model of RNG together with some extensions to the models presented in the literature and shows comparative results on three well-known and accepted datasets. The experimental results obtained show that, under specific circumstances, RNG-based reservoirs can achieve better performance.
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study
Venue: arXiv cs.SI 2018
Year: 2018

Extracting textual features from tweets is a challenging process due to the noisy nature of the content and the weak signal of most of the words used. In this paper, we propose using singular value decomposition (SVD) with clustering to enhance the signals of the textual features in the tweets to improve the correlation with events. The proposed technique applies SVD to the time series vector for each feature to factorize the matrix of feature/day counts, in order to ensure the independence of the feature vectors. Afterwards, the k-means clustering is applied to build a look-up table that maps members of each cluster to the cluster-centroid. The lookup table is used to map each feature in the original data to the centroid of its cluster, then we calculate the sum of the term frequency vectors of all features in each cluster to the term-frequency-vector of the cluster centroid. To test the technique we calculated the correlations of the cluster centroids with the golden standard record (GSR) vector before and after summing the vectors of the cluster members to the centroid-vector. The proposed method is applied to multiple correlation techniques including the Pearson, Spearman, distance correlation and Kendal Tao. The experiments have also considered the different word forms and lengths of the features including keywords, n-grams, skip-grams and bags-of-words. The correlation results are enhanced significantly as the highest correlation scores have increased from 0.3 to 0.6, and the average correlation scores have increased from 0.3 to 0.4.
Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection Technique for Crypto-ransomware Early Detection
Venue: arXiv cs.CR 2018
Year: 2018

Crypto-ransomware is characterized by its irreversible effect even after the detection and removal. As such, the early detection is crucial to protect user data and files of being held to ransom. Several solutions have proposed utilizing the data extracted during the initial phases of the attacks before the encryption takes place. However, the lack of enough data at the early phases of the attack along with high dimensional features space renders the model prone to overfitting which decreases its detection accuracy. To this end, this paper proposed a novel redundancy coefficient gradual up-weighting approach that was incorporated to the calculation of redundancy term of mutual information to improve the feature selection process and enhance the accuracy of the detection model. Several machine learning classifiers were used to evaluate the detection performance of the proposed techniques. The experimental results show that the accuracy of proposed techniques achieved higher detection accuracy. Those results demonstrate the efficacy of the proposed techniques for the early detection tasks.
Finding Better Subword Segmentation for Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

For different language pairs, word-level neural machine translation (NMT) models with a fixed-size vocabulary suffer from the same problem of representing out-of-vocabulary (OOV) words. The common practice usually replaces all these rare or unknown words with a <UNK> token, which limits the translation performance to some extent. Most of recent work handled such a problem by splitting words into characters or other specially extracted subword units to enable open-vocabulary translation. Byte pair encoding (BPE) is one of the successful attempts that has been shown extremely competitive by providing effective subword segmentation for NMT systems. In this paper, we extend the BPE style segmentation to a general unsupervised framework with three statistical measures: frequency (FRQ), accessor variety (AV) and description length gain (DLG). We test our approach on two translation tasks: German to English and Chinese to English. The experimental results show that AV and DLG enhanced systems outperform the FRQ baseline in the frequency weighted schemes at different significant levels.
Continuous-Time Markov Decisions based on Partial Exploration
Venue: arXiv cs.SY 2018
Year: 2018

We provide a framework for speeding up algorithms for time-bounded reachability analysis of continuous-time Markov decision processes. The principle is to find a small, but almost equivalent subsystem of the original system and only analyse the subsystem. Candidates for the subsystem are identified through simulations and iteratively enlarged until runs are represented in the subsystem with high enough probability. The framework is thus dual to that of abstraction refinement. We instantiate the framework in several ways with several traditional algorithms and experimentally confirm orders-of-magnitude speed ups in many cases.
Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs
Venue: arXiv cs.DC 2018
Year: 2018

For a deep learning model, efficient execution of its computation graph is key to achieving high performance. Previous work has focused on improving the performance for individual nodes of the computation graph, while ignoring the parallelization of the graph as a whole. However, we observe that running multiple operations simultaneously without interference is critical to efficiently perform parallelizable small operations. The attempt of executing the computation graph in parallel in deep learning frameworks usually involves much resource contention among concurrent operations, leading to inferior performance on manycore CPUs. To address these issues, in this paper, we propose Graphi, a generic and high-performance execution engine to efficiently execute a computation graph in parallel on manycore CPUs. Specifically, Graphi minimizes the interference on both software/hardware resources, discovers the best parallel setting with a profiler, and further optimizes graph execution with the critical-path first scheduling. Our experiments show that the parallel execution consistently outperforms the sequential one. The training times on four different neural networks with Graphi are 2.1x to 9.5x faster than those with TensorFlow on a 68-core Intel Xeon Phi processor.
The Time-SIFT method : detecting 3-D changes from archival photogrammetric analysis with almost exclusively image information
Venue: arXiv cs.CV 2018
Year: 2018

Archival aerial imagery is a source of worldwide very high resolution data for documenting paste 3-D changes. However, external information is required so that accurate 3-D models can be computed from archival aerial imagery. In this research, we propose and test a new method, termed Time-SIFT (Scale Invariant Feature Transform), which allows for computing coherent multi-temporal Digital Elevation Models (DEMs) with almost exclusively image information. This method is based on the invariance properties of the SIFT-like methods which are at the root of the Structure from Motion (SfM) algorithms. On a test site of 170 km2, we applied SfM algorithms to a unique image block with all the images of four different dates covering forty years. We compared this method to more classical methods based on the use of affordable additional data such as ground control points collected in recent orthophotos. We did extensive tests to determine which processing choices were most impacting on the final result. With these tests, we aimed at evaluating the potential of the proposed Time-SIFT method for the detection and mapping of 3-D changes. Our study showed that the Time-SIFT method was the prime criteria that allowed for computing informative DEMs of difference with almost exclusively image information and limited photogrammetric expertise and human intervention. Due to the fact that the proposed Time-SIFT method can be automatically applied with exclusively image information, our results pave the way to a systematic processing of the archival aerial imagery on very large spatio-temporal windows, and should hence greatly help the unlocking of archival aerial imagery for the documenting of past 3-D changes.
Distributed Lifetime Optimization in Wireless Sensor Networks using Alternating Direction Method of Multipliers
Venue: arXiv cs.NI 2018
Year: 2018

Due to the limited energy of sensor nodes in wireless sensor networks, extending the networks lifetime is a major challenge that can be formulated as an optimization problem. In this paper, we propose a distributed iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) with the aim of maximizing sensor network lifetime. The features of this algorithm are use of local information, low overhead of message passing, low computational complexity, fast convergence, and consequently reduced energy consumption. In this study, we present the convergence results and the number of iterations required to achieve the stopping criterion. Furthermore, the impact of problem size (number of sensor nodes) on the solution and constraints violation is studied and finally, the proposed algorithm is compared to one of the well-known subgradient-based algorithms.
Jamming in multiple independent Gaussian channels as a game
Venue: arXiv cs.GT 2018
Year: 2018

We study the problem of \emph{jamming} in multiple independent \emph{Gaussian channels} as a zero-sum game. We show that in the unique Nash equilibrium of the game the best-response strategy of the transmitter is the \emph{waterfilling} to the sum of the jamming and the noise power in each channel and the best-response strategy of the jammer is the \emph{waterfilling} only to the noise power.
A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
Venue: arXiv cs.CE 2018
Year: 2018

In this paper, a new data-driven multiscale material modeling method, which we refer to as deep material network, is developed based on mechanistic homogenization theory of representative volume element (RVE) and advanced machine learning techniques. We propose to use a collection of connected mechanistic building blocks with analytical homogenization solutions which avoids the loss of essential physics in generic neural networks, and this concept is demonstrated for 2-dimensional RVE problems and network depth up to 7. Based on linear elastic RVE data from offline direct numerical simulations, the material network can be effectively trained using stochastic gradient descent with backpropagation algorithm, enhanced by model compression methods. Importantly, the trained network is valid for any local material laws without the need for additional calibration or micromechanics assumption. Its extrapolations to unknown material and loading spaces for a wide range of problems are validated through numerical experiments, including linear elasticity with high contrast of phase properties, nonlinear history-dependent plasticity and finite-strain hyperelasticity under large deformations. By discovering a proper topological representation of RVE with fewer degrees of freedom, this intelligent material model is believed to open new possibilities of high-fidelity efficient concurrent simulations for a large-scale heterogeneous structure. It also provides a mechanistic understanding of structure-property relations across material length scales and enables the development of parameterized microstructural database for material design and manufacturing.
Applying Domain Randomization to Synthetic Data for Object Category Detection
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pre-trained on standard datasets and fine-tune them for specific application domains with a small set of domain relevant images. In this work, we show that using synthetic datasets that are not necessarily photo-realistic can be a better alternative to simply fine-tune pre-trained networks. Specifically, our results show an impressive 25% improvement in the mAP metric over a fine-tuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.
Evaluating Creativity in Computational Co-Creative Systems
Venue: arXiv cs.AI 2018
Year: 2018

This paper provides a framework for evaluating creativity in co-creative systems: those that involve computer programs collaborating with human users on creative tasks. We situate co-creative systems within a broader context of computational creativity and explain the unique qualities of these systems. We present four main questions that can guide evaluation in co-creative systems: Who is evaluating the creativity, what is being evaluated, when does evaluation occur and how the evaluation is performed. These questions provide a framework for comparing how existing co-creative systems evaluate creativity, and we apply them to examples of co-creative systems in art, humor, games and robotics. We conclude that existing co-creative systems tend to focus on evaluating the user experience. Adopting evaluation methods from autonomous creative systems may lead to co-creative systems that are self-aware and intentional.