OpenRaiser/Intern-Atlas huggingface dataset:
Enforcing public data archiving policies in academic publishing: A study of ecology journals
Venue: arXiv cs.CY 2018
Year: 2018

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator
Venue: arXiv cs.CL 2018
Year: 2018

The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we applied a speech chain mechanism as a semi-supervised learning. It provides the ability for ASR and TTS to assist each other when they receive unpaired data and let them infer the missing pair and optimize the model with reconstruction loss. If we only have speech without transcription, ASR generates the most likely transcription from the speech data, and then TTS uses the generated transcription to reconstruct the original speech features. However, in previous papers, we just limited our back-propagation to the closest module, which is the TTS part. One reason is that back-propagating the error through the ASR is challenging due to the output of the ASR are discrete tokens, creating non-differentiability between the TTS and ASR. In this paper, we address this problem and describe how to thoroughly train a speech chain end-to-end for reconstruction loss using a straight-through estimator (ST). Experimental results revealed that, with sampling from ST-Gumbel-Softmax, we were able to update ASR parameters and improve the ASR performances by 11\% relative CER reduction compared to the baseline.
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance
Venue: arXiv cs.CV 2018
Year: 2018

As object recognition becomes an increasingly common ML task, and recent research demonstrating CNNs vulnerability to attacks and small image perturbations necessitate fully understanding the foundations of object recognition. We focus on understanding the mechanisms behind how neural networks generalize to spatial transformations of complex objects. While humans excel at discriminating between objects shown at new positions, orientations, and scales, past results demonstrate that this may be limited to familiar objects - humans demonstrate low tolerance of spatial-variances for purposefully constructed novel objects. Because training artificial neural networks from scratch is similar to showing novel objects to humans, we seek to understand the factors influencing the tolerance of CNNs to spatial transformations. We conduct a thorough empirical examination of seven Convolutional Neural Network (CNN) architectures. By training on a controlled face image dataset, we measure model accuracy across different degrees of 5 transformations: position, size, rotation, Gaussian blur, and resolution transformation due to resampling. We also examine how learning strategy affects generalizability by examining how different amounts of pre-training have on model robustness. Overall, we find that the most significant contributor to transformation invariance is pre-training on a large, diverse image dataset. Moreover, while AlexNet tends to be the least robust network, VGG and ResNet architectures demonstrate higher robustness for different transformations. Along with kernel visualizations and qualitative analyses, we examine differences between learning strategy and inherent architectural properties in contributing to invariance of transformations, providing valuable information towards understanding how to achieve greater robustness to transformations in CNNs.
Cardinalities estimation under sliding time window by sharing HyperLogLog Counter
Venue: arXiv cs.NI 2018
Year: 2018

Cardinalities estimation is an important research topic in network management and security. How to solve this problem under sliding time window is a hot topic. HyperLogLog is a memory efficient algorithm work under a fixed time window. A sliding version of HyperLogLog can work under sliding time window by replacing every counter of HyperLogLog with a list of feature possible maxim (LFPM). But LFPM is a dynamic structure whose size is variable at running time. This paper proposes a novel counter for HyperLogLog which consumes smaller size of memory than that of LFPM. Our counter is called bit distance recorder BDR, because it maintains the distance of every left most "1" bit position. The size of BDR is fixed. Based on BDR, we design a multi hosts' cardinalities estimation algorithm under sliding time window, virtual bit distance recorder VBDR. VBDR allocate a virtual vector of BDR for every host and every physical BDR is shared by several hosts to improve the memory usage. After a small modifcation, we propose another two parallel versions of VBDR which can run on GPU to handle high speed traffic. One of these parallel VBDR is fast in IP pair scanning and the other one is memory efficient. BDR is also suitable for other cardinality estimation algorithms such as PCSA, LogLog.
Coalitional Game Based Carpooling Algorithms for Quality of Experience
Venue: arXiv cs.NI 2018
Year: 2018

Carpooling service is an effective solution to balance the limited number of taxicabs and the soaring demands from users, Thus, how to motivate more passengers to participate in carpooling is essential, especially in extreme weather or in rush hours. Most of existing works focus on improving the availability, convenience and security of carpooling service, while ignoring to guarantee the quality of experience of passengers. In this work, we focus on how to fulfill the expected sojourn time of passengers in carpooling service using coalition game. We formulate the QoE guarantee problem as a benefit allocation problem. To solve the problem, we quantify the impatience of passengers due to detouring time delay, depending on their own expected sojourn time and expected compensation per unit time of delay. The algorithm named PCA is proposed to minimize the impatience of all passengers, under which we calculate the compensation for passengers based on Shapley value. We prove that PCA can guarantee the fairness of passengers. Extensive simulation results demonstrate that PCA can minimize the impatience of passengers. Moreover, compared with the existing algorithm DST, PCA can reduce the payment of each passenger by 14.4 percent averagely with only 13.3 percent loss of driver's revenue. However, the least expected revenue of the driver can still be fulfilled, which produces a win-win solution for both passengers and drivers in carpooling.
SIEVE: Helping Developers Sift Wheat from Chaff via Cross-Platform Analysis
Venue: arXiv cs.IR 2018
Year: 2018

Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing cross-platform analysis. Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related contents. We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.
Fully-Connected vs. Sub-Connected Hybrid Precoding Architectures for mmWave MU-MIMO
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to its low power consumption with respect to its digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures for HDA MU-MIMO, i.e., the fully-connected (FC) architecture where each RF antenna port is connected to all antenna elements of the array, and the one-stream-per-subarray (OSPS) architecture where the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition phase and data communication phase, such that the latter takes place by using the beam direction information obtained in the former phase. For each phase, we propose our own BA and precoding schemes that outperform the counterparts in the literature. We also evaluate the power efficiency of the two HDA architectures taking into account the practical hardware impairments, e.g., the power dissipation at different hardware components as well as the potential power backoff under typical power amplifier (PA) constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, but the OSPS architecture outperforms the FC case in terms of hardware complexity and power efficiency, only at the cost of a slightly longer time of initial beam acquisition.
A Decision Support Framework for Manufacturing Improvement and Relocation Prevention in Thailand: Supply Chain Perspective
Venue: arXiv cs.CY 2018
Year: 2018

The low economic growth and competition among neighbouring countries has caused Foreign Direct nvestments (FDIs) to relocate their businesses. In order to prevent further business relocation, this aper proposes an integrated framework based on the supply chain to help analyse decision making for plant situations and enhance manufacturing perrformance. The context of this perspective is pplied to manufacturers located in the industrial state region of Lumphun province, Thailand. Data collection and review of literature was used to identify the factors that influence industrial investment. The SCOR model was used to define the parameters, which here then used in Arena simulation. The simulation needs to describe factors affected on industrial erformance. From this result, an integrated analysis model was built and the importance of supply chain collaboration was identified. A Multi Agent System (MAS) was suggested to enhance of the effective nteraction between supply chain' agents. It is in order to mitigate risks among them.
Secure Communication over Interference Channel: To Jam or Not to Jam?
Venue: arXiv cs.IT 2018
Year: 2018

We consider a secure communication over a two-user Gaussian interference channel, where each transmitter sends a confidential message to its legitimate receiver. For this setting, we identify a regime where the simple scheme of using Gaussian wiretap codebook at each transmitter (without cooperative jamming) and treating interference as noise at each intended receiver (in short, GWC-TIN scheme) achieves the optimal secure sum capacity to within a constant gap. For the symmetric case, this simple scheme is optimal when the interference-to-signal ratio (all link strengths in decibel scale) is no more than 2/3. However, when the ratio is more than 2/3, we show that this simple scheme is not optimal anymore and a scheme with cooperative jamming is proposed to achieve the optimal secure sum generalized degrees-of-freedom (GDoF). Specifically, for the symmetric case, we complete the optimal secure sum GDoF characterization for all the interference regimes.
An Empirical Analysis of Vulnerabilities in Python Packages for Web Applications
Venue: arXiv cs.SE 2018
Year: 2018

This paper examines software vulnerabilities in common Python packages used particularly for web development. The empirical dataset is based on the PyPI package repository and the so-called Safety DB used to track vulnerabilities in selected packages within the repository. The methodological approach builds on a release-based time series analysis of the conditional probabilities for the releases of the packages to be vulnerable. According to the results, many of the Python vulnerabilities observed seem to be only modestly severe; input validation and cross-site scripting have been the most typical vulnerabilities. In terms of the time series analysis based on the release histories, only the recent past is observed to be relevant for statistical predictions; the classical Markov property holds.
Stochastic Submodular Cover with Limited Adaptivity
Venue: arXiv cs.DS 2018
Year: 2018

In the submodular cover problem, we are given a non-negative monotone submodular function $f$ over a ground set $E$ of items, and the goal is to choose a smallest subset $S \subseteq E$ such that $f(S) = Q$ where $Q = f(E)$. In the stochastic version of the problem, we are given $m$ stochastic items which are different random variables that independently realize to some item in $E$, and the goal is to find a smallest set of stochastic items whose realization $R$ satisfies $f(R) = Q$. The problem captures as a special case the stochastic set cover problem and more generally, stochastic covering integer programs. We define an $r$-round adaptive algorithm to be an algorithm that chooses a permutation of all available items in each round $k \in [r]$, and a threshold $τ_k$, and realizes items in the order specified by the permutation until the function value is at least $τ_k$. The permutation for each round $k$ is chosen adaptively based on the realization in the previous rounds, but the ordering inside each round remains fixed regardless of the realizations seen inside the round. Our main result is that for any integer $r$, there exists a poly-time $r$-round adaptive algorithm for stochastic submodular cover whose expected cost is $\tilde{O}(Q^{{1}/{r}})$ times the expected cost of a fully adaptive algorithm. Prior to our work, such a result was not known even for the case of $r=1$ and when $f$ is the coverage function. On the other hand, we show that for any $r$, there exist instances of the stochastic submodular cover problem where no $r$-round adaptive algorithm can achieve better than $Ω(Q^{{1}/{r}})$ approximation to the expected cost of a fully adaptive algorithm. Our lower bound result holds even for coverage function and for algorithms with unbounded computational power.
Variation in research collaboration patterns across academic ranks
Venue: arXiv cs.DL 2018
Year: 2018

The ability to activate and manage effective collaborations is becoming an increasingly important criteria in policies on academic career advancement. The rise of such policies leads to development of indicators that permit measurement of the propensity to collaborate for academics of different ranks, and to examine the role of several variables in collaboration, first among these being the researchers' disciplines. In this work we apply an innovative bibliometric approach based on individual propensity for collaboration to measure the differences in propensity across academic ranks, by discipline and for choice of collaboration forms - intramural, extramural domestic and international. The analysis is based on the scientific production of Italian academics for the period 2006 to 2010, totaling over 200,000 publications indexed in Web of Science. It shows that assistant professors register a propensity for intramural collaboration that is clearly greater than for professors of higher ranks. Vice versa, the higher ranks, but not quite so clearly, register greater propensity to collaborate at the international level.
A bibliometric tool to assess the regional dimension of university-industry research collaborations
Venue: arXiv cs.DL 2018
Year: 2018

The present work proposes a bibliometric methodology for measuring the grade of correspondence between regional industry's demand for research collaboration and supply from public laboratories. The methodology also permits measurement of the intensity and direction of the regional flows of knowledge in public-private collaborations. The aim is to provide a diagnostic instrument for regional and national policy makers, which could add to existing ones to plan interventions for re-balancing sectorial public supply of knowledge with industrial absorptive capacity, and maximizing appropriability of knowledge spillovers. The methodology is applied to university-industry collaborations in the hard sciences in all Italian administrative regions.
Extracting Linguistic Resources from the Web for Concept-to-Text Generation
Venue: arXiv cs.CL 2018
Year: 2018

Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly. Focusing on NaturalOWL, a publicly available state of the art natural language generator for OWL ontologies, we propose methods to extract from the Web sentence plans and natural language names, two of the most important types of domain-specific linguistic resources used by the generator. Experiments show that texts generated using linguistic resources extracted by our methods in a semi-automatic manner, with minimal human involvement, are perceived as being almost as good as texts generated using manually authored linguistic resources, and much better than texts produced by using linguistic resources extracted from the relation and entity identifiers of the ontology.
BlockReduce: Scaling Blockchain to Human Commerce
Venue: arXiv cs.CR 2018
Year: 2018

Blockchains have shown great promise as peer-to-peer digital currency systems over the past 10 years. However, with increased popularity, the demand for processing transactions has also grown leading to increased costs, confirmation times, and limited blockchain utility. There have been a number of proposals on how to scale blockchains, such as Plasma, Polkadot, Elastico, RapidChain, Bitcoin-NG, and OmniLedger. These solutions all propose the segmentation of every function of a blockchain, namely consensus, permanent data storage, transaction processing, and consistency, which significantly increases the complexity and difficulty of implementation. BlockReduce is a new blockchain structure which only segments consistency, allowing it to scale to handle tens of thousands of transactions per second without impacting fault tolerance or decentralization. Moreover, BlockReduce will significantly decrease node bandwidth requirements and network latency through incentives while simultaneously minimizing other resource demands in order to prevent centralization of nodes.
DOLORES: Deep Contextualized Knowledge Graph Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note that short paths on knowledge graphs comprising of chains of entities and relations can encode valuable information regarding their contextual usage. We operationalize this notion by representing knowledge graphs not as a collection of triples but as a collection of entity-relation chains, and learn embeddings for entities and relations using deep neural models that capture such contextual usage. In particular, our model is based on Bi-Directional LSTMs and learn deep representations of entities and relations from constructed entity-relation chains. We show that these representations can very easily be incorporated into existing models to significantly advance the state of the art on several knowledge graph prediction tasks like link prediction, triple classification, and missing relation type prediction (in some cases by at least 9.5%).
Mixture Density Generative Adversarial Networks
Venue: arXiv cs.LG 2018
Year: 2018

Generative Adversarial Networks have surprising ability for generating sharp and realistic images, though they are known to suffer from the so-called mode collapse problem. In this paper, we propose a new GAN variant called Mixture Density GAN that while being capable of generating high-quality images, overcomes this problem by encouraging the Discriminator to form clusters in its embedding space, which in turn leads the Generator to exploit these and discover different modes in the data. This is achieved by positioning Gaussian density functions in the corners of a simplex, using the resulting Gaussian mixture as a likelihood function over discriminator embeddings, and formulating an objective function for GAN training that is based on these likelihoods. We demonstrate empirically (1) the quality of the generated images in Mixture Density GAN and their strong similarity to real images, as measured by the Fréchet Inception Distance (FID), which compares very favourably with state-of-the-art methods, and (2) the ability to avoid mode collapse and discover all data modes.
Survey on Vision-based Path Prediction
Venue: arXiv cs.CV 2018
Year: 2018

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.