OpenRaiser/Intern-Atlas huggingface dataset:
From Crowdsourcing to Crowdmining: Using Implicit Human Intelligence for Better Understanding of Crowdsourced Data
Venue: arXiv cs.HC 2019
Year: 2019

With the development of mobile social networks, more and more crowdsourced data are generated on the Web or collected from real-world sensing. The fragment, heterogeneous, and noisy nature of online/offline crowdsourced data, however, makes it difficult to be understood. Traditional content-based analyzing methods suffer from potential issues such as computational intensiveness and poor performance. To address them, this paper presents CrowdMining. In particular, we observe that the knowledge hidden in the process of data generation, regarding individual/crowd behavior patterns (e.g., mobility patterns, community contexts such as social ties and structure) and crowd-object interaction patterns (flickering or tweeting patterns) are neglected in crowdsourced data mining. Therefore, a novel approach that leverages implicit human intelligence (implicit HI) for crowdsourced data mining and understanding is proposed. Two studies titled CrowdEvent and CrowdRoute are presented to showcase its usage, where implicit HIs are extracted either from online or offline crowdsourced data. A generic model for CrowdMining is further proposed based on a set of existing studies. Experiments based on real-world datasets demonstrate the effectiveness of CrowdMining.
Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Venue: arXiv cs.CV 2019
Year: 2019

Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing frameworks rely on Class Activation Sequence (CAS) to localize actions by minimizing the video-level classification loss, which exploits the most discriminative parts of actions but ignores the minor regions. In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits. Specifically, the first module is designed as a well-designed Seeded Sequence Growing (SSG) Network for progressively extending seed regions (namely the highly reliable regions initialized by a CAS-based framework) to their expected boundaries. The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG. In this way, a whole network composed of these two modules can be trained in an adversarial manner. The goal of the adversary is to mine features that are difficult for the action classifier. That is, erasion from SSG will force the classifier to discover minor or even new action regions on the input feature sequence, and the classifier will drive the seeds to grow, alternately. At last, we could obtain the action locations and categories from the well-trained SSG and the classifier. Extensive experiments on two public benchmarks THUMOS'14 and ActivityNet1.3 demonstrate the impressive performance of our proposed method compared with the state-of-the-arts.
Age-Optimal Power Control for Status Update Systems with Packet Based Transmissions
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the average age of information (AoI) minimization in status update systems in which the update packets are transmitted with fixed rate. It is assumed that the source avoids queue-induced delay by generating and sending a new status update packet in each time slot. Assuming that the transmit power can be adapted based on the number of successive NACKs (negative acknowledgements) received, a closed-form expression for the average AoI is derived considering transmissions over block fading channel models. An optimization problem for minimizing the average AoI under average power constraints is formulated and a stochastic optimization algorithm for obtaining a feasible solution is proposed. Numerical results on the proposed power control policy show that more power is preferred to be allocated to packets after a certain number of failure packets in the low-power regime and the proposed power control reduces the average AoI compared to the constant power policy. An alternative on-off power control policy is also proposed and shown to achieve satisfactory performance.
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations
Venue: arXiv cs.CV 2019
Year: 2019

Adversarial examples contain small perturbations that can remain imperceptible to human observers but alter the behavior of even the best performing deep learning models and yield incorrect outputs. Since their discovery, adversarial examples have drawn significant attention in machine learning: researchers try to reveal the reasons for their existence and improve the robustness of machine learning models to adversarial perturbations. The state-of-the-art defense is the computationally expensive and very time consuming adversarial training via projected gradient descent (PGD). We hypothesize that adversarial attacks exploit the open space risk of classic monotonic activation functions. This paper introduces the tent activation function with bounded open space risk and shows that tents make deep learning models more robust to adversarial attacks. We demonstrate on the MNIST dataset that a classifier with tents yields an average accuracy of 91.8% against six white-box adversarial attacks, which is more than 15 percentage points above the state of the art. On the CIFAR-10 dataset, our approach improves the average accuracy against the six white-box adversarial attacks to 73.5% from 41.8% achieved by adversarial training via PGD.
TinySearch -- Semantics based Search Engine using Bert Embeddings
Venue: arXiv cs.IR 2019
Year: 2019

Existing search engines use keyword matching or tf-idf based matching to map the query to the web-documents and rank them. They also consider other factors such as page rank, hubs-and-authority scores, knowledge graphs to make the results more meaningful. However, the existing search engines fail to capture the meaning of query when it becomes large and complex. BERT, introduced by Google in 2018, provides embeddings for words as well as sentences. In this paper, I have developed a semantics-oriented search engine using neural networks and BERT embeddings that can search for query and rank the documents in the order of the most meaningful to least meaningful. The results shows improvement over one existing search engine for complex queries for given set of documents.
Embedding-based system for the Text part of CALL v3 shared task
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents a scoring system that has shown the top result on the text subset of CALL v3 shared task. The presented system is based on text embeddings, namely NNLM~\cite{nnlm} and BERT~\cite{Bert}. The distinguishing feature of the given approach is that it does not rely on the reference grammar file for scoring. The model is compared against approaches that use the grammar file and proves the possibility to achieve similar and even higher results without a predefined set of correct answers. The paper describes the model itself and the data preparation process that played a crucial role in the model training.
Mesh Variational Autoencoders with Edge Contraction Pooling
Venue: arXiv cs.GR 2019
Year: 2019

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation, shape interpolation and shape embedding.
Testing convexity of functions over finite domains
Venue: arXiv cs.CC 2019
Year: 2019

We establish new upper and lower bounds on the number of queries required to test convexity of functions over various discrete domains. 1. We provide a simplified version of the non-adaptive convexity tester on the line. We re-prove the upper bound $O(\frac{\log(εn)}ε)$ in the usual uniform model, and prove an $O(\frac{\log n}ε)$ upper bound in the distribution-free setting. 2. We show a tight lower bound of $Ω(\frac{\log(εn)}ε)$ queries for testing convexity of functions $f: [n] \rightarrow \mathbb{R}$ on the line. This lower bound applies to both adaptive and non-adaptive algorithms, and matches the upper bound from item 1, showing that adaptivity does not help in this setting. 3. Moving to higher dimensions, we consider the case of a stripe $[3] \times [n]$. We construct an \emph{adaptive} tester for convexity of functions $f\colon [3] \times [n] \to \mathbb R$ with query complexity $O(\log^2 n)$. We also show that any \emph{non-adaptive} tester must use $Ω(\sqrt{n})$ queries in this setting. Thus, adaptivity yields an exponential improvement for this problem. 4. For functions $f\colon [n]^d \to \mathbb R$ over domains of dimension $d \geq 2$, we show a non-adaptive query lower bound $Ω((\frac{n}{d})^{\frac{d}{2}})$.
Economic Power, Population, and the Size of Astronomical Community
Venue: arXiv cs.DL 2019
Year: 2019

The number of astronomers for a country registered to the IAU is known to have a correlation with the GDP. However, the robustness of this relationship can be doubted, because the fraction of astronomers joining the IAU differs from country to country. Here we revisit this correlation by using the recent data updated as of 2017, and then we find a similar correlation by using the total enumeration of astronomers and astrophysicists with PhD degrees and working in each country, instead of adopting the number of IAU members. We confirm the existence of two subgroup in the correlation. One group consists of European advanced countries having long history of modern astronomy, while the other group consists of countries having experienced recent rapid economic development. In order to find causation in the correlation, we obtain the long-term variations of the number of astronomers, population, and the GDP for a number of countries to find that the number of astronomers per citizen for recently developing countries has increased more rapidly as GDP per capita increased, than that for fully developed countries. We collect a demographic data of the Korean astronomical community. From these findings we estimate the proper size of the Korean astronomical community by considering the society's economic power and population. The current number of PhD astronomers working in Korea is approximately 310, but it should be 550 that is large enough to be comparable and competitive to the sizes of Spainish, Canadian, and Japanese astronomical communities. We discuss on the way how to overcome the vulnerability of the Korean astronomical community, based on the statistics of national R&D expenditure structure comparing with that of other major advanced countries.
The power of dynamic social networks to predict individuals' mental health
Venue: arXiv cs.SI 2019
Year: 2019

Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our knowledge, we are the first to do this. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without developing a predictive model; or they study other individual traits but not mental health. In a systematic and comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data.
Towards Requirements for a Demand Side Response Energy Management System for Households
Venue: arXiv cs.CY 2019
Year: 2019

Demand response is considered to be one of the key means through which peak energy demand could be ameliorated. This report presents a requirements elicitation exercise (undertaken in collaboration with the Bristol City Council, UK) to elicit the requirements that a smart appliance automation service for domestic energy demand response management must address to be accepted by the households. The study comprises of an interview study with 28 householders, the findings from which were validated through two co-design workshops.
A Generic Efficient Biased Optimizer for Consensus Protocols
Venue: arXiv cs.DC 2019
Year: 2019

Consensus is one of the most fundamental distributed computing problems. In particular, it serves as a building block in many replication based fault-tolerant systems and in particular in multiple recent blockchain solutions. Depending on its exact variant and other environmental assumptions, solving consensus requires multiple communication rounds. Yet, there are known optimistic protocols that guarantee termination in a single communication round under favorable conditions. In this paper we present a generic optimizer than can turn any consensus protocol into an optimized protocol that terminates in a single communication round whenever all nodes start with the same predetermined value and no Byzantine failures occur (although node crashes are allowed). This is regardless of the network timing assumptions and additional oracle capabilities assumed by the base consensus protocol being optimized. In the case of benign failures, our optimizer works whenever the number of faulty nodes $f<n/2$. For Byzantine behavior, our optimizer's resiliency depends on the validity variant sought. In the case of classical validity, it can accommodate $f<n/4$ Byzantine failures. With the more recent external validity function assumption, it works whenever $f<n/3$. Either way, our optimizer only relies on oral messages, thereby imposing very light-weight crypto requirements.
Rendering Point Clouds with Compute Shaders
Venue: arXiv cs.GR 2019
Year: 2019

We propose a compute shader based point cloud rasterizer with up to 10 times higher performance than classic point-based rendering with the GL_POINT primitive. In addition to that, our rasterizer offers 5 byte depth-buffer precision with uniform or customizable distribution, and we show that it is possible to implement a high-quality splatting method that blends together overlapping fragments while still maintaining higher frame-rates than the traditional approach.
Addressing Limited Weight Resolution in a Fully Optical Neuromorphic Reservoir Computing Readout
Venue: arXiv cs.ET 2019
Year: 2019

Using optical hardware for neuromorphic computing has become more and more popular recently due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer. One of them is that, depending on the technology used, optical weighting elements may not share the same resolution as in the electrical domain. Moreover, noise and drift are important considerations as well. In this article, we investigate a new method for improving the performance of optical weighting, even in the presence of noise and in the case of very low resolution. Even with only 8 to 32 levels of resolution, the method can outperform the naive traditional low-resolution weighting by several orders of magnitude in terms of bit error rate and can deliver performance very close to full-resolution weighting elements, also in noisy environments.
Parallel Finger Search Structures
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we present two versions of a parallel finger structure FS on p processors that supports searches, insertions and deletions, and has a finger at each end. This is to our knowledge the first implementation of a parallel search structure that is work-optimal with respect to the finger bound and yet has very good parallelism (within a factor of O( (log p)^2 ) of optimal). We utilize an extended implicit batching framework that transparently facilitates the use of FS by any parallel program P that is modelled by a dynamically generated DAG D where each node is either a unit-time instruction or a call to FS. The total work done by either version of FS is bounded by the finger bound F[L] (for some linearization L of D ), i.e. each operation on an item with finger distance r takes O( log r + 1 ) amortized work; it is cheaper for items closer to a finger. Running P using the simpler version takes O( ( T[1] + F[L] ) / p + T[inf] + d * ( (log p)^2 + log n ) ) time on a greedy scheduler, where T[1],T[inf] are the size and span of D respectively, and n is the maximum number of items in FS, and d is the maximum number of calls to FS along any path in D. Using the faster version, this is reduced to O( ( T[1] + F[L] ) / p + T[inf] + d * (log p)^2 + s[L] ) time, where s[L] is the weighted span of D where each call to FS is weighted by its cost according to F[L]. We also sketch how to extend FS to support a fixed number of movable fingers. The data structures in our paper fit into the dynamic multithreading paradigm, and their performance bounds are directly composable with other data structures given in the same paradigm. Also, the results can be translated to practical implementations using work-stealing schedulers.
Multi-owner Secure Encrypted Search Using Searching Adversarial Networks
Venue: arXiv cs.CR 2019
Year: 2019

Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $\mathcal{O}(\log N)$. In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.
Exploring the Intersections of Web Science and Accessibility
Venue: arXiv cs.IR 2019
Year: 2019

The web is the prominent way information is exchanged in the 21st century. However, ensuring web-based information is accessible is complicated, particularly with web applications that rely on JavaScript and other technologies to deliver and build representations; representations are often the HTML, images, or other code a server delivers for a web resource. Static representations are becoming rarer and assessing the accessibility of web-based information to ensure it is available to all users is increasingly difficult given the dynamic nature of representations. In this work, we survey three ongoing research threads that can inform web accessibility solutions: assessing web accessibility, modeling web user activity, and web application crawling. Current web accessibility research is continually focused on increasing the percentage of automatically testable standards, but still relies heavily upon manual testing for complex interactive applications. Along-side web accessibility research, there are mechanisms developed by researchers that replicate user interactions with web pages based on usage patterns. Crawling web applications is a broad research domain; exposing content in web applications is difficult because of incompatibilities in web crawlers and the technologies used to create the applications. We describe research on crawling the deep web by exercising user forms. We close with a thought exercise regarding the convergence of these three threads and the future of automated, web-based accessibility evaluation and assurance through a use case in web archiving. These research efforts provide insight into how users interact with websites, how to automate and simulate user interactions, how to record the results of user interactions, and how to analyze, evaluate, and map resulting website content to determine its relative accessibility.
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we describe the details of the neural dependency parser sub-mitted by our team to the NLPCC 2019 Shared Task of Semi-supervised do-main adaptation subtask on Cross-domain Dependency Parsing. Our system is based on the stack-pointer networks(STACKPTR). Considering the im-portance of context, we utilize self-attention mechanism for the representa-tion vectors to capture the meaning of words. In addition, to adapt three dif-ferent domains, we utilize neural network based deep transfer learning which transfers the pre-trained partial network in the source domain to be a part of deep neural network in the three target domains (product comments, product blogs and web fiction) respectively. Results on the three target domains demonstrate that our model performs competitively.
Towards Generating Stylized Image Captions via Adversarial Training
Venue: arXiv cs.CV 2019
Year: 2019

While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e.g., incorporating positive or negative sentiment). However, because the stylistic component is typically the last part of training, current models usually pay more attention to the style at the expense of accurate content description. In addition, there is a lack of variability in terms of the stylistic aspects. To address these issues, we propose an image captioning model called ATTEND-GAN which has two core components: first, an attention-based caption generator to strongly correlate different parts of an image with different parts of a caption; and second, an adversarial training mechanism to assist the caption generator to add diverse stylistic components to the generated captions. Because of these components, ATTEND-GAN can generate correlated captions as well as more human-like variability of stylistic patterns. Our system outperforms the state-of-the-art as well as a collection of our baseline models. A linguistic analysis of the generated captions demonstrates that captions generated using ATTEND-GAN have a wider range of stylistic adjectives and adjective-noun pairs.