OpenRaiser/Intern-Atlas huggingface dataset:
"This is why we play": Characterizing Online Fan Communities of the NBA Teams
Venue: arXiv cs.SI 2018
Year: 2018

Professional sports constitute an important part of people's modern life. People spend substantial amounts of time and money supporting their favorite players and teams, and sometimes even riot after games. However, how team performance affects fan behavior remains understudied at a large scale. As almost every notable professional team has its own online fan community, these communities provide great opportunities for investigating this research question. In this work, we provide the first large-scale characterization of online fan communities of professional sports teams. Since user behavior in these online fan communities is inherently connected to game events and team performance, we construct a unique dataset that combines 1.5M posts and 43M comments in NBA-related communities on Reddit with statistics that document team performance in the NBA. We analyze the impact of team performance on fan behavior both at the game level and the season level. First, we study how team performance in a game relates to user activity during that game. We find that surprise plays an important role: the fans of the top teams are more active when their teams lose and so are the fans of the bottom teams in an unexpected win. Second, we study fan behavior over consecutive seasons and show that strong team performance is associated with fans of low loyalty, likely due to "bandwagon fans." Fans of the bottom teams tend to discuss their team's future such as young talents in the roster, which may help them stay optimistic during adversity. Our results not only contribute to understanding the interplay between online sports communities and offline context but also provide significant insights into sports management.
CIDPro: Custom Instructions for Dynamic Program Diversification
Venue: arXiv cs.CR 2018
Year: 2018

Timing side-channel attacks pose a major threat to embedded systems due to their ease of accessibility. We propose CIDPro, a framework that relies on dynamic program diversification to mitigate timing side-channel leakage. The proposed framework integrates the widely used LLVM compiler infrastructure and the increasingly popular RISC-V FPGA soft-processor. The compiler automatically generates custom instructions in the security critical segments of the program, and the instructions execute on the RISC-V custom co-processor to produce diversified timing characteristics on each execution instance. CIDPro has been implemented on the Zynq7000 XC7Z020 FPGA device to study the performance overhead and security tradeoffs. Experimental results show that our solution can achieve 80% and 86% timing side-channel capacity reduction for two benchmarks with an acceptable performance overhead compared to existing solutions. In addition, the proposed method incurs only a negligible hardware area overhead of 1% slices of the entire RISC-V system.
Optimized edge-based grasping method for a cluttered environment
Venue: arXiv cs.RO 2018
Year: 2018

This paper looks into the problem of grasping region localization along with suitable pose from a cluttered environment without any a priori knowledge of the object geometry. This end-to-end method detects the handles from a single frame of input sensor. The pipeline starts with the creation of multiple surface segments to detect the required gap in the first stage, and eventually helps in detecting boundary lines. Our novelty lies in the fact that we have merged color based edge and depth edge in order to get more reliable boundary points through which a pair of boundary line is fitted. Also this information is used to validate the handle by measuring the angle between the boundary lines and also by checking for amy potential occlusion. In addition, we also proposed an optimizing cost function based method to choose the best handle from a set of valid handles. The method proposed is tested on real-life datasets and is found to out form state of the art methods in terms of precision.
A Threat Modeling Framework for Evaluating Computing Platforms Against Architectural Attacks
Venue: arXiv cs.CR 2018
Year: 2018

software component misuse a privileged relationship with the hardware to by pass system protections, monitors, or forensic tools. These relationships are often not illegal and exist between system components by design. Hence, even a system with secure hardware and software components, can be architecturally vulnerable. Unfortunately, the existing threat modeling schemes are not applicable for modeling architectural attacks against computing platforms. This is mostly because the existing techniques rely on an abstract representation of a software (.e.g., Data Flow Diagram) as a primary requirement which is not available for a platform as a whole (considering both hardware and software elements). In this paper, we have discussed the necessity of a hardware-software architectural view to system threat modeling. Then, we have proposed Lamellae, a framework adapts threat modeling method to be applicable for untrusted platforms by a holistic approach. Lamellae involves system security architecture for abstract modeling of the platforms. Using the Design structure matrix analysis, Lamellae helps an end-user to identify possible attack vectors against a platform. The framework is a connection point of concepts from system engineering and software security domains. We have applied the framework on a multi-purpose computer with x86-64 architecture as a case-study to show the effectiveness of our framework.
IKA: Independent Kernel Approximator
Venue: arXiv cs.LG 2018
Year: 2018

This paper describes a new method for low rank kernel approximation called IKA. The main advantage of IKA is that it produces a function $ψ(x)$ defined as a linear combination of arbitrarily chosen functions. In contrast the approximation produced by Nyström method is a linear combination of kernel evaluations. The proposed method consistently outperformed Nyström method in a comparison on the STL-10 dataset. Numerical results are reproducible using the source code available at https://gitlab.com/matteo-ronchetti/IKA
Semantic Human Matting
Venue: arXiv cs.CV 2018
Year: 2018

Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes them difficult to be applied to large scale data or time-sensitive scenarios. In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints learned from data and propose an automatic human matting algorithm (SHM). SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and fine details is challenging. We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha matte. We also construct a very large dataset with high quality annotations consisting of 35,513 unique foregrounds to facilitate the learning and evaluation of human matting. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods.
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
Venue: arXiv cs.LG 2018
Year: 2018

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.
Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach
Venue: arXiv cs.DC 2018
Year: 2018

Compared with relational database (RDB), graph database (GDB) is a more intuitive expression of the real world. Each node in the GDB is a both storage and logic unit. Since it is connected to its neighboring nodes through edges, and its neighboring information could be easily obtained in one-step graph traversal. It is able to conduct local computation independently and all nodes can do their local work in parallel. Then the whole system can be maximally analyzed and assessed in parallel to largely improve the computation performance without sacrificing the precision of final results. This paper firstly introduces graph database, power system graph modeling and potential graph computing applications in power systems. Two iterative methods based on graph database and PageRank are presented and their convergence are discussed. Vertex contraction is proposed to improve the performance by eliminating zero-impedance branch. A combination of the two iterative methods is proposed to make use of their advantages. Testing results based on a provincial 1425-bus system demonstrate that the proposed comprehensive approach is a good candidate for power flow analysis.
An Elementary Proof of a Classical Information-Theoretic Formula
Venue: arXiv cs.IT 2018
Year: 2018

A renowned information-theoretic formula by Shannon expresses the mutual information rate of a white Gaussian channel with a stationary Gaussian input as an integral of a simple function of the power spectral density of the channel input. We give in this paper a rigorous yet elementary proof of this classical formula. As opposed to all the conventional approaches, which either rely on heavy mathematical machineries or have to resort to some "external" results, our proof, which hinges on a recently proven sampling theorem, is elementary and self-contained, only using some well-known facts from basic calculus and matrix theory.
Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery
Venue: arXiv cs.CV 2018
Year: 2018

Object detection and classification of traffic signs in street-view imagery is an essential element for asset management, map making and autonomous driving. However, some traffic signs occur rarely and consequently, they are difficult to recognize automatically. To improve the detection and classification rates, we propose to generate images of traffic signs, which are then used to train a detector/classifier. In this research, we present an end-to-end framework that generates a realistic image of a traffic sign from a given image of a traffic sign and a pictogram of the target class. We propose a residual attention mechanism with dense concatenation called Dense Residual Attention, that preserves the background information while transferring the object information. We also propose to utilize multi-scale discriminators, so that the smaller scales of the output guide the higher resolution output. We have performed detection and classification tests across a large number of traffic sign classes, by training the detector using the combination of real and generated data. The newly trained model reduces the number of false positives by 1.2 - 1.5% at 99% recall in the detection tests and an absolute improvement of 4.65% (top-1 accuracy) in the classification tests.
Theoretical analysis and propositions for "ontology citation"
Venue: arXiv cs.OH 2018
Year: 2018

Ontology citation, the practice of referring the ontology in a similar fashion the scientific community routinely follows in providing the bibliographic references to other scholarly works, has not received enough attention it supposed to. Interestingly, so far none of the existing standard citation styles (e.g., APA, CMOS, and IEEE) have included ontology as a citable information source in the list of citable information sources such as journal article, book, website, etc. Also, not much work can be found in the literature on this topic though there are various issues and aspects of it that demand a thorough study. For instance, what to cite? Is it the publication that describes the ontology, or the ontology itself? The citation format, style, illustration of motivations of ontology citation, the citation principles, ontology impact factor, citation analysis, and so forth. In this work, we primarily analyse the current ontology citation practices and the related issues. We illustrate the various motivations and the basic principles of ontology citation. We also propose a template for referring the source of ontologies.
Chinese Discourse Segmentation Using Bilingual Discourse Commonality
Venue: arXiv cs.CL 2018
Year: 2018

Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just through discriminating the functions of punctuations. In this paper, we argue that Chinese EDUs may not end at the punctuation positions and should follow the definition of EDU in RST-DT. With this definition, we conduct Chinese discourse segmentation with the help of English labeled data.Using discourse commonality between English and Chinese, we design an adversarial neural network framework to extract common language-independent features and language-specific features which are useful for discourse segmentation, when there is no or only a small scale of Chinese labeled data available. Experiments on discourse segmentation demonstrate that our models can leverage common features from bilingual data, and learn efficient Chinese-specific features from a small amount of Chinese labeled data, outperforming the baseline models.
Traffic Density Estimation using a Convolutional Neural Network
Venue: arXiv cs.LG 2018
Year: 2018

The goal of this project is to introduce and present a machine learning application that aims to improve the quality of life of people in Singapore. In particular, we investigate the use of machine learning solutions to tackle the problem of traffic congestion in Singapore. In layman's terms, we seek to make Singapore (or any other city) a smoother place. To accomplish this aim, we present an end-to-end system comprising of 1. A traffic density estimation algorithm at traffic lights/junctions and 2. a suitable traffic signal control algorithms that make use of the density information for better traffic control. Traffic density estimation can be obtained from traffic junction images using various machine learning techniques (combined with CV tools). After research into various advanced machine learning methods, we decided on convolutional neural networks (CNNs). We conducted experiments on our algorithms, using the publicly available traffic camera dataset published by the Land Transport Authority (LTA) to demonstrate the feasibility of this approach. With these traffic density estimates, different traffic algorithms can be applied to minimize congestion at traffic junctions in general.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Venue: arXiv cs.HC 2018
Year: 2018

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In this work, we present GAN Lab, the first interactive visualization tool designed for non-experts to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, users can interactively train generative models and visualize the dynamic training process's intermediate results. GAN Lab tightly integrates an model overview graph that summarizes GAN's structure, and a layered distributions view that helps users interpret the interplay between submodels. GAN Lab introduces new interactive experimentation features for learning complex deep learning models, such as step-by-step training at multiple levels of abstraction for understanding intricate training dynamics. Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web browsers, without the need for installation or specialized hardware, overcoming a major practical challenge in deploying interactive tools for deep learning.
Bicomp: A Bilayer Scalable Nakamoto Consensus Protocol
Venue: arXiv cs.NI 2018
Year: 2018

Blockchain has received great attention in recent years and motivated innovations in different scenarios. However, many vital issues which affect its performance are still open. For example, it is widely convinced that high level of security and scalability and full decentralization are still impossible to achieve simultaneously. In this paper, we propose Bicomp, a bilayer scalable Nakamoto consensus protocol, which is an approach based on high security and pure decentralized Nakamoto consensus, and with a significant improvement on scalability. In Bicomp, two kinds of blocks are generated, i.e., microblocks for concurrent transaction packaging in network, and macroblocks for leadership competition and chain formation. A leader is elected at beginning of each round by using a macroblock header from proof-of-work. An elected leader then receives and packages multiple microblocks mined by different nodes into one macroblock during its tenure, which results in a bilayer block structure. Such design limits a leader's power and encourages as many nodes as possible to participate in the process of packaging transactions, which promotes the sharding nature of the system. Furthermore, several mechanisms are carefully designed to reduce transaction overlapping and further limit a leader's power, among which a novel transaction diversity based metric is proposed as the second level criteria besides the longest-chain-first principle on selecting a legitimate chain when fork happens. Security issues and potential attacks to Bicomp are extensively discussed and experiments for evaluation are performed. From the experimental results based on 50 nodes all over the world, Bicomp achieves significant improvement on scalability than that of Bitcoin and Ethereum, while the security and decentralization merits are still preserved.
An Algebra of Lightweight Ontologies
Venue: arXiv cs.AI 2018
Year: 2018

This paper argues that certain ontology design problems are profitably addressed by treating ontologies as theories and by defining a set of operations that create new ontologies, including their constraints, out of other ontologies. The paper first shows how to use the operations in the context of ontology reuse, how to take advantage of the operations to compare different ontologies, or different versions of an ontology, and how the operations may help design mediated schemas in a bottom up fashion. The core of the paper discusses how to compute the operations for lightweight ontologies and addresses the question of minimizing the set of constraints of a lightweight ontology. Finally, the paper describes an implementation of the operations, as a Protégé plug-in.
Ranking RDF Instances in Degree-decoupled RDF Graphs
Venue: arXiv cs.DB 2018
Year: 2018

In the last decade, RDF emerged as a new kind of standardized data model, and a sizable body of knowledge from fields such as Information Retrieval was adapted to RDF graphs. One common task in graph databases is to define an importance score for nodes based on centrality measures, such as PageRank and HITS. The majority of the strategies highly depend on the degree of the node. However, in some RDF graphs, called degree-decoupled RDF graphs, the notion of importance is not directly related to the node degree. Therefore, this work first proposes three novel node importance measures, named InfoRank I, II and III, for degree-decoupled RDF graphs. It then compares the proposed measures with traditional PageRank and other familiar centrality measures, using with an IMDb dataset.
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
Venue: arXiv cs.CR 2018
Year: 2018

Nowadays, machine learning based Automatic Speech Recognition (ASR) technique has widely spread in smartphones, home devices, and public facilities. As convenient as this technology can be, a considerable security issue also raises -- the users' speech content might be exposed to malicious ASR monitoring and cause severe privacy leakage. In this work, we propose HASP -- a high-performance security enhancement approach to solve this security issue on mobile devices. Leveraging ASR systems' vulnerability to the adversarial examples, HASP is designed to cast human imperceptible adversarial noises to real-time speech and effectively perturb malicious ASR monitoring by increasing the Word Error Rate (WER). To enhance the practical performance on mobile devices, HASP is also optimized for effective adaptation to the human speech characteristics, environmental noises, and mobile computation scenarios. The experiments show that HASP can achieve optimal real-time security enhancement: it can lead an average WER of 84.55% for perturbing the malicious ASR monitoring, and the data processing speed is 15x to 40x faster compared to the state-of-the-art methods. Moreover, HASP can effectively perturb various ASR systems, demonstrating a strong transferability.
Design and Realization of Intersection Traffic Control Simulation System Using Connected Vehicle Technology
Venue: arXiv cs.SY 2018
Year: 2018

The connected vehicle technology is a remarkable trend in the field of the intelligent transportation system. Since the actual deployment of the connected vehicle system is still lacking hitherto, simulation is widely adopted as the major method of verification in related researches. Although traditional commercial traffic simulation systems perform well in macroscopic simulations, they seem redundant in the circumstance of small-size traffic. In addition, those systems are unable to simulate the communication between vehicles and road-side infrastructures. This study designs a platform for the simulation of intersection traffic control using connected vehicle technology. By providing an abundance of customizable parameters, the platform can simulate at various levels of speed to meet different requirements, serving as a basis for testing further research.