OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task
Venue: arXiv cs.CL 2018
Year: 2018

In this survey paper, we have evaluated several recent deep neural network (DNN) architectures on a TIMIT phone recognition task. We chose the TIMIT corpus due to its popularity and broad availability in the community. It also simulates a low-resource scenario that is helpful in minor languages. Also, we prefer the phone recognition task because it is much more sensitive to an acoustic model quality than a large vocabulary continuous speech recognition (LVCSR) task. In recent years, many DNN published papers reported results on TIMIT. However, the reported phone error rates (PERs) were often much higher than a PER of a simple feed-forward (FF) DNN. That was the main motivation of this paper: To provide a baseline DNNs with open-source scripts to easily replicate the baseline results for future papers with lowest possible PERs. According to our knowledge, the best-achieved PER of this survey is better than the best-published PER to date.
TxPI-u: A Resource for Personality Identification of Undergraduates
Venue: arXiv cs.CL 2018
Year: 2018

Resources such as labeled corpora are necessary to train automatic models within the natural language processing (NLP) field. Historically, a large number of resources regarding a broad number of problems are available mostly in English. One of such problems is known as Personality Identification where based on a psychological model (e.g. The Big Five Model), the goal is to find the traits of a subject's personality given, for instance, a text written by the same subject. In this paper we introduce a new corpus in Spanish called Texts for Personality Identification (TxPI). This corpus will help to develop models to automatically assign a personality trait to an author of a text document. Our corpus, TxPI-u, contains information of 416 Mexican undergraduate students with some demographics information such as, age, gender, and the academic program they are enrolled. Finally, as an additional contribution, we present a set of baselines to provide a comparison scheme for further research.
Parallel Nonnegative CP Decomposition of Dense Tensors
Venue: arXiv cs.NA 2018
Year: 2018

The CP tensor decomposition is a low-rank approximation of a tensor. We present a distributed-memory parallel algorithm and implementation of an alternating optimization method for computing a CP decomposition of dense tensor data that can enforce nonnegativity of the computed low-rank factors. The principal task is to parallelize the matricized-tensor times Khatri-Rao product (MTTKRP) bottleneck subcomputation. The algorithm is computation efficient, using dimension trees to avoid redundant computation across MTTKRPs within the alternating method. Our approach is also communication efficient, using a data distribution and parallel algorithm across a multidimensional processor grid that can be tuned to minimize communication. We benchmark our software on synthetic as well as hyperspectral image and neuroscience dynamic functional connectivity data, demonstrating that our algorithm scales well to 100s of nodes (up to 4096 cores) and is faster and more general than the currently available parallel software.
Private Information Retrieval from Coded Storage Systems with Colluding, Byzantine, and Unresponsive Servers
Venue: arXiv cs.IT 2018
Year: 2018

The problem of Private Information Retrieval (PIR) from coded storage systems with colluding, byzantine, and unresponsive servers is considered. An explicit scheme using an $[n,k]$ Reed-Solomon storage code is designed, protecting against $t$-collusion and handling up to $b$ byzantine and $r$ unresponsive servers, when $n>k+t+2b+r-1$. This scheme achieves a PIR rate of $\frac{n-r-(k+2b+t-1)}{n-r}$. In the case where the capacity is known, namely when $k=1$, it is asymptotically capacity-achieving as the number of files grows. Lastly, the scheme is adapted to symmetric PIR.
Hardness and algorithmic results for the approximate cover problem
Venue: arXiv cs.DS 2018
Year: 2018

In CPM 2017, Amir et al. introduce a problem, named \emph{approximate string cover} (\textbf{ACP}), motivated by many aplications including coding and automata theory, formal language theory, combinatorics and molecular biology. A \emph{cover} of a string $T$ is a string $C$ for which every letter of $T$ lies within some occurrence of $C$. The input of the \textbf{ACP} problem consists of a string $T$ and an integer $m$ (less than the length of $T$), and the goal is to find a string $C$ of length $m$ that covers a string $T'$ which is as close to $T$ as possible (under some predefined distance). Amir et al. study the problem for the Hamming distance. In this paper we continue the work of Amir et al. and show the following results: We show an approximation algorithm for the \textbf{ACP} with an approximation ratio of $\sqrt{OPT}$, where OPT is the size of the optimal solution. We provide an FPT algorithm with respect to the alphabet size. \item The \textbf{ACP} problem naturally extends to pseudometrics. Moreover, we show that for some family of pseudometrics, that we term \emph{homogenous additive pseudometrics}, the complexity of \textbf{ACP} remains unchanged. We partially give an answer to an open problem of Amir et al. and show that the Hamming distance over an unbounded alphabet is equivalent to an extended metric over a fixed sized alphabet.
Awareness and Experience of Developers to Outdated and License-Violating Code on Stack Overflow: An Online Survey
Venue: arXiv cs.SE 2018
Year: 2018

We performed two online surveys of Stack Overflow answerers and visitors to assess their awareness to outdated code and software licenses in Stack Overflow answerers. The answerer survey targeted 607 highly reputed Stack Overflow users and received a high response rate of 33%. Our findings are as follows. Although most of the code snippets in the answers are written from scratch, there are code snippets cloned from the corresponding questions, from personal or company projects, or from open source projects. Stack Overflow answerers are aware that some of their snippets are outdated. However, 19% of the participants report that they rarely or never fix their outdated code. At least 98% of the answerers never include software licenses in their snippets and 69% never check for licensing conflicts with Stack Overflow's CC BY-SA 3.0 if they copy the code from other sources to Stack Overflow answers. The visitor survey uses convenient sampling and received 89 responses. We found that 66% of the participants experienced a problem from cloning and reusing Stack Overflow snippets. Fifty-six percent of the visitors never reported the problems back to the Stack Overflow post. Eighty-five percent of the participants are not aware that StackOverflow applies the CC BY-SA 3.0 license, and sixty-two percent never give attributions to Stack Overflow posts or answers they copied the code from. Moreover, 66% of the participants do not check for licensing conflicts between the copied Stack Overflow code and their software. With these findings, we suggest Stack Overflow raise awareness of their users, both answerers and visitors, to the problem of outdated and license-violating code snippets.
Synaptic partner prediction from point annotations in insect brains
Venue: arXiv cs.CV 2018
Year: 2018

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method.
Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
Venue: arXiv cs.IR 2018
Year: 2018

In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements. However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior. For example, search engine traffic for florists usually tends to peak around Valentine's day, Mother's day, etc. To overcome, this challenge, in this manuscript we propose three models which are able to incorporate the effects arising due to variations in product demand. The proposed models are a combination of product demand features, specialized data sampling methodologies and ensemble techniques. We demonstrate the performance of our proposed models on datasets obtained from a real-world setting. Our results show that the proposed models more accurately predict the outcome of users interactions with product related advertisements while simultaneously being robust to fluctuations in user and advertiser behaviors.
Semi-supervised Seizure Prediction with Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised seizure prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.
Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid
Venue: arXiv cs.SY 2018
Year: 2018

Flexible energy resources can help balance the power grid by providing different types of ancillary services. However, the balancing potential of most types of resources is restricted by physical constraints such as the size of their energy buffer, limits on power-ramp rates, or control delays. Using the example of Secondary Frequency Regulation, this paper shows how the flexibility of various resources can be exploited more efficiently by considering multiple resources with complementary physical properties and controlling them in a coordinated way. To this end, optimal adjustable control policies are computed based on robust optimization. Our problem formulation takes into account power ramp-rate constraints explicitly, and accurately models the different timescales and lead times of the energy and reserve markets. Simulations demonstrate that aggregations of select resources can offer significantly more regulation capacity than the resources could provide individually.
COZMO-A New Lightweight Stream Cipher
Venue: arXiv cs.CR 2018
Year: 2018

This paper deals with the merger of the two lightweight stream ciphers: A5by1 and Trivium. The idea is to make the key stream generation more secure and to remove the attacks of the individual algorithms. The bits generated by the Trivium cipher will act as the input of the A5by1 cipher. The registers used in the A5by1 cipher will be filled by the output bits of the Trivium cipher. The three registers will then be connected to generate an output which will be our required key stream.
Impact Zone Analysis of p-Cycle
Venue: arXiv cs.NI 2018
Year: 2018

Pre-configured cycle (p-Cycle) method has been studied in literature extensively for optical network protection. A large p-cycle has high capacity efficiency and can protect a large number of nodes against the single link failure scenarios. All the links protected by such a p-cycle lose protection when the p-cycle is consumed to restore traffic after a failure. As the probability of multiple link failure is high for a large network, it also means that with higher probability, on the second failure, protection may not be there for the failed link. Thus, if the number of links protected by a p-cycle is large, it makes the network unprotected with high probability on the advent of the second failure. In this paper, we study the impact zone due to a first link failure in the various configurations of the p-cycles. The study gives insight into how to choose the p-cycle configuration to reduce the impact zone while using minimum spare capacity. We propose few methods and compare them to show how the impact zone analysis can be used to improve the fault tolerance in an optical network.
Optimising finite-difference methods for PDEs through parameterised time-tiling in Devito
Venue: arXiv cs.PF 2018
Year: 2018

Finite-difference methods are widely used in solving partial differential equations. In a large problem set, approximations can take days or weeks to evaluate, yet the bulk of computation may occur within a single loop nest. The modelling process for researchers is not straightforward either, requiring models with differential equations to be translated into stencil kernels, then optimised separately. One tool that seeks to speed up and eliminate mistakes from this tedious procedure is Devito, used to efficiently employ finite-difference methods. In this work, we implement time-tiling, a loop nest optimisation, in Devito yielding a decrease in runtime of up to 45%, and at least 20% across stencils from the acoustic wave equation family, widely used in Devito's target domain of seismic imaging. We present an estimator for arithmetic intensity under time-tiling and a model to predict runtime improvements in stencil computations. We also consider generalisation of time-tiling to imperfect loop nests, a less widely studied problem.
Oh, What a Fragile Web We Weave: Third-party Service Dependencies In Modern Webservices and Implications
Venue: arXiv cs.NI 2018
Year: 2018

The recent October 2016 DDoS attack on Dyn served as a wakeup call to the security community as many popular and independent webservices (e.g., Twitter, Spotify) were impacted. This incident raises a larger question on the fragility of modern webservices due to their dependence on third-party services. In this paper, we characterize the dependencies of popular webservices on third party services and how these can lead to DoS, RoQ attacks, and reduction in security posture. In particular, we focus on three critical infrastructure services: DNS, CDNs, and certificate authorities (CAs). We analyze both direct relationships (e.g., Twitter uses Dyn) and indirect dependencies (e.g., Netflix uses Symantec as OCSP and Symantec, in turn, uses Verisign for DNS). Our key findings are: (1) 73.14% of the top 100,000 popular services are vulnerable to reduction in availabil- ity due to potential attacks on third-party DNS, CDN, CA services that they exclusively rely on; (2) the use of third-party services is concentrated, so that if the top-10 providers of CDN, DNS and OCSP services go down, they can potentially impact 25%-46% of the top 100K most popular web services; (3) transitive depen- dencies significantly increase the set of webservices that exclusively depend on popular CDN and DNS service providers, in some cases by ten times (4) targeting even less popular webservices can potentially cause signifi- cant collateral damage, affecting upto 20% of the top- 100K webservices due to their shared dependencies. Based on our findings, we present a number of key implications and guidelines to guard against such Internet- scale incidents in the future.
Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing
Venue: arXiv cs.CG 2018
Year: 2018

Dimensionality reduction is an integral part of data visualization. It is a process that obtains a structure preserving low-dimensional representation of the high-dimensional data. Two common criteria can be used to achieve a dimensionality reduction: distance preservation and topology preservation. Inspired by recent work in topological data analysis, we are on the quest for a dimensionality reduction technique that achieves the criterion of homology preservation, a generalized version of topology preservation. Specifically, we are interested in using topology-inspired manifold landmarking and manifold tearing to aid such a process and evaluate their effectiveness.
Paragraph-based complex networks: application to document classification and authenticity verification
Venue: arXiv cs.CL 2018
Year: 2018

With the increasing number of texts made available on the Internet, many applications have relied on text mining tools to tackle a diversity of problems. A relevant model to represent texts is the so-called word adjacency (co-occurrence) representation, which is known to capture mainly syntactical features of texts.In this study, we introduce a novel network representation that considers the semantic similarity between paragraphs. Two main properties of paragraph networks are considered: (i) their ability to incorporate characteristics that can discriminate real from artificial, shuffled manuscripts and (ii) their ability to capture syntactical and semantic textual features. Our results revealed that real texts are organized into communities, which turned out to be an important feature for discriminating them from artificial texts. Interestingly, we have also found that, differently from traditional co-occurrence networks, the adopted representation is able to capture semantic features. Additionally, the proposed framework was employed to analyze the Voynich manuscript, which was found to be compatible with texts written in natural languages. Taken together, our findings suggest that the proposed methodology can be combined with traditional network models to improve text classification tasks.
Personalized Thread Recommendation for MOOC Discussion Forums
Venue: arXiv cs.SI 2018
Year: 2018

Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facilitated through discussion forums hosted by course providers. In this paper, we propose a probabilistic model for the process of learners posting on such forums, using point processes. Different from existing works, our method integrates topic modeling of the post text, timescale modeling of the decay in post activity over time, and learner topic interest modeling into a single model, and infers this information from user data. Our method also varies the excitation levels induced by posts according to the thread structure, to reflect typical notification settings in discussion forums. We experimentally validate the proposed model on three real-world MOOC datasets, with the largest one containing up to 6,000 learners making 40,000 posts in 5,000 threads. Results show that our model excels at thread recommendation, achieving significant improvement over a number of baselines, thus showing promise of being able to direct learners to threads that they are interested in more efficiently. Moreover, we demonstrate analytics that our model parameters can provide, such as the timescales of different topic categories in a course.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
Venue: arXiv cs.HC 2018
Year: 2018

Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
Venue: arXiv cs.CV 2018
Year: 2018

We present a new data-driven video inpainting method for recovering missing regions of video frames. A novel deep learning architecture is proposed which contains two sub-networks: a temporal structure inference network and a spatial detail recovering network. The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, which performs image-based inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution. Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames. Our method jointly trains both sub-networks in an end-to-end manner. We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods.
Optimal Design of Virtual Inertia and Damping Coefficients for Virtual Synchronous Machines
Venue: arXiv cs.SY 2018
Year: 2018

Increased penetration of inverter-connected renewable energy sources (RES) in the power system has resulted in a decrease in available rotational inertia which serves as an immediate response to frequency deviation due to disturbances. The concept of virtual inertia has been proposed to combat this decrease by enabling the inverters to produce active power in response to a frequency deviation like a synchronous generator. In this paper, we present an algorithm to optimally design the inertia and damping coefficient required for an inverter-based virtual synchronous machine (VSM) to participate efficiently in the inertia response portion of primary frequency control. We design the objective function to explicitly trade-off between competing objectives such as the damping rate the the frequency nadir. Specifically, we formulate the design problem as a constrained and regularized H2 norm minimization problem, and develop an efficient gradient algorithm for this non-convex problem. This proposed algorithm is applied to a test case to demonstrate its performance against existing methods.