OpenRaiser/Intern-Atlas huggingface dataset:
Understanding the Role of Intermediaries in Online Social E-commerce: An Exploratory Study of Beidian
Venue: arXiv cs.CY 2020
Year: 2020

Social e-commerce, as a new form of social computing based marketing platforms, utilizes existing real-world social relationships for promotions and sales of products. It has been growing rapidly in recent years and attracted tens of millions of users in China. A key group of actors who enable market transactions on these platforms are intermediaries who connect producers with consumers by sharing information with and recommending products to their real-world social contacts. Despite their crucial role, the nature and behavior of these intermediaries on these social e-commerce platforms has not been systematically analyzed. Here we address this knowledge gap through a mixed method study. Leveraging 9 months' all-round behavior of about 40 million users on Beidian -- one of the largest social e-commerce sites in China, alongside with qualitative evidence from online forums and interviews, we examine characteristics of intermediaries, identify their behavioral patterns and uncover strategies and mechanisms that make successful intermediaries. We demonstrate that intermediaries on social e-commerce sites act as local trend detectors and "social grocers". Furthermore, successful intermediaries are highly dedicated whenever best sellers appear and broaden items for promotion. To the best of our knowledge, this paper presents the first large-scale analysis on the emerging role of intermediaries in social e-commerce platforms, which provides potential insights for the design and management of social computing marketing platforms.
LiMITS: An Effective Approach for Trajectory Simplification
Venue: arXiv cs.DB 2020
Year: 2020

Trajectories represent the mobility of moving objects and thus is of great value in data mining applications. However, trajectory data is enormous in volume, so it is expensive to store and process the raw data directly. Trajectories are also redundant so data compression techniques can be applied. In this paper, we propose effective algorithms to simplify trajectories. We first extend existing algorithms by replacing the commonly used $L_2$ metric with the $L_\infty$ metric so that they can be generalized to high dimensional space (e.g., 3-space in practice). Next, we propose a novel approach, namely L-infinity Multidimensional Interpolation Trajectory Simplification (LiMITS). LiMITS belongs to weak simplification and takes advantage of the $L_\infty$ metric. It generates simplified trajectories by multidimensional interpolation. It also allows a new format called compact representation to further improve the compression ratio. Finally, We demonstrate the performance of LiMITS through experiments on real-world datasets, which show that it is more effective than other existing methods.
Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
Venue: arXiv cs.AI 2020
Year: 2020

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL methods using knowledge-infused learning, which is one of the strategies. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches, and illustrate it with examples from natural language processing for healthcare and education applications.
Just-in-Time Learning for Bottom-Up Enumerative Synthesis
Venue: arXiv cs.PL 2020
Year: 2020

A key challenge in program synthesis is the astronomical size of the search space the synthesizer has to explore. In response to this challenge, recent work proposed to guide synthesis using learned probabilistic models. Obtaining such a model, however, might be infeasible for a problem domain where no high-quality training data is available. In this work we introduce an alternative approach to guided program synthesis: instead of training a model ahead of time we show how to bootstrap one just in time, during synthesis, by learning from partial solutions encountered along the way. To make the best use of the model, we also propose a new program enumeration algorithm we dub guided bottom-up search, which extends the efficient bottom-up search with guidance from probabilistic models. We implement this approach in a tool called Probe, which targets problems in the popular syntax-guided synthesis (SyGuS) format. We evaluate Probe on benchmarks from the literature and show that it achieves significant performance gains both over unguided bottom-up search and over a state-of-the-art probability-guided synthesizer, which had been trained on a corpus of existing solutions. Moreover, we show that these performance gains do not come at the cost of solution quality: programs generated by Probe are only slightly more verbose than the shortest solutions and perform no unnecessary case-splitting.
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Venue: arXiv cs.CL 2020
Year: 2020

Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation framework dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization objective is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2% while applied to the RoBERTa-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training base-lines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework.
Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering
Venue: arXiv cs.CV 2020
Year: 2020

Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57\% using fewer parameters on VQA-v2.0 test-standard split.
A method to evaluate the reliability of social media data for social network analysis
Venue: arXiv cs.SI 2020
Year: 2020

To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Real-time High-Quality Rendering of Non-Rotating Black Holes
Venue: arXiv cs.GR 2020
Year: 2020

We propose a real-time method to render high-quality images of a non-rotating black hole with an accretion disc and background stars. Our method is based on beam tracing, but uses precomputed tables to find the intersections of each curved light beam with the scene in constant time per pixel. It also uses a specific texture filtering scheme to integrate the contribution of the light sources to each beam. Our method is simple to implement and achieves high frame rates.
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic
Venue: arXiv cs.LG 2020
Year: 2020

Over the past few years, deep neural networks have shown excellent results in multiple tasks, however, there is still an increasing need to address the problem of interpretability to improve model transparency, performance, and safety. Achieving eXplainable Artificial Intelligence (XAI) by combining neural networks with continuous logic and multi-criteria decision-making tools is one of the most promising ways to approach this problem: by this combination, the black-box nature of neural models can be reduced. The continuous logic-based neural model uses so-called Squashing activation functions, a parametric family of functions that satisfy natural invariance requirements and contain rectified linear units as a particular case. This work demonstrates the first benchmark tests that measure the performance of Squashing functions in neural networks. Three experiments were carried out to examine their usability and a comparison with the most popular activation functions was made for five different network types. The performance was determined by measuring the accuracy, loss, and time per epoch. These experiments and the conducted benchmarks have proven that the use of Squashing functions is possible and similar in performance to conventional activation functions. Moreover, a further experiment was conducted by implementing nilpotent logical gates to demonstrate how simple classification tasks can be solved successfully and with high performance. The results indicate that due to the embedded nilpotent logical operators and the differentiability of the Squashing function, it is possible to solve classification problems, where other commonly used activation functions fail.
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed DVICE network and a novel few-shot learning approach to tackle the small sample size problem encountered in co-segmentation with small datasets like iCoseg and MSRC. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation over multiple datasets using only a small volume of training data, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.
Modeling Support for Domain-Specific Application Definition
Venue: arXiv cs.SE 2020
Year: 2020

In this paper we present the modeling support infrastructure for domain-specific application definition. This consists of a set of meta-models and the associated generators to allow the definition of reusable and domain-specific behavior blocks, which can later be used to compose complex behaviors. In addition we also present the related visual languages that facilitate the creation of these models.
Modeling and Implementation of Quadcopter Autonomous Flight Based on Alternative Methods to Determine Propeller Parameters
Venue: arXiv cs.RO 2020
Year: 2020

To properly simulate and implement a quadcopter flight control for intended load and flight conditions, the quadcopter model must have parameters on various relationships including propeller thrust-torque, thrust-PWM, and thrust--angular speed to a certain level of accuracy. Thrust-torque modeling requires an expensive reaction torque measurement sensor. In the absence of sophisticated equipment, the study comes up with alternative methods to complete the quadcopter model. The study also presents a method of modeling the rotational aerodynamic drag on the quadcopter. Although the resulting model of the reaction torque generated by the quadcopter's propellers and the model of the drag torque acting on the quadcopter body that are derived using the methods in this study may not yield the true values of these quantities, the experimental modeling techniques presented in this work ensure that the derived dynamic model for the quadcopter will nevertheless behave identically with the true model for the quadcopter. The derived dynamic model is validated by basic flight controller simulation and actual flight implementation. The model is used as basis for a quadcopter design, which eventually is used for test purposes of basic flight control. This study serves as a baseline for fail-safe control of a quadcopter experiencing an unexpected motor failure.
An individual-level ground truth dataset for home location detection
Venue: arXiv cs.CY 2020
Year: 2020

Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data. Despite its widespread use, home detection relies on a few assumptions that are difficult to check without ground truth, i.e., where the individual that owns the device resides. In this paper, we provide an unprecedented evaluation of the accuracy of home detection algorithms on a group of sixty-five participants for whom we know their exact home address and the antennas that might serve them. Besides, we analyze not only Call Detail Records (CDRs) but also two other mobile phone streams: eXtended Detail Records (XDRs, the ``data'' channel) and Control Plane Records (CPRs, the network stream). These data streams vary not only in their temporal granularity but also they differ in the data generation mechanism', e.g., CDRs are purely human-triggered while CPR is purely machine-triggered events. Finally, we quantify the amount of data that is needed for each stream to carry out successful home detection for each stream. We find that the choice of stream and the algorithm heavily influences home detection, with an hour-of-day algorithm for the XDRs performing the best, and with CPRs performing best for the amount of data needed to perform home detection. Our work is useful for researchers and practitioners in order to minimize data requests and to maximize the accuracy of home antenna location.
Against Scale: Provocations and Resistances to Scale Thinking
Venue: arXiv cs.CY 2020
Year: 2020

At the heart of what drives the bulk of innovation and activity in Silicon Valley and elsewhere is scalability. This unwavering commitment to scalability -- to identify strategies for efficient growth -- is at the heart of what we refer to as "scale thinking." Whether people are aware of it or not, scale thinking is all-encompassing. It is not just an attribute of one's product, service, or company, but frames how one thinks about the world (what constitutes it and how it can be observed and measured), its problems (what is a problem worth solving versus not), and the possible technological fixes for those problems. This paper examines different facets of scale thinking and its implication on how we view technology and collaborative work. We argue that technological solutions grounded in scale thinking are unlikely to be as liberatory or effective at deep, systemic change as their purveyors imagine. Rather, solutions which resist scale thinking are necessary to undo the social structures which lie at the heart of social inequality. We draw on recent work on mutual aid networks and propose questions to ask of collaborative work systems as a means to evaluate technological solutions and guide designers in identifying sites of resistance to scale thinking.
A Game AI Competition to foster Collaborative AI research and development
Venue: arXiv cs.AI 2020
Year: 2020

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A competition is also a way to motivate new researchers to study these challenges. In this document, we present the Geometry Friends Game AI Competition. Geometry Friends is a two-player cooperative physics-based puzzle platformer computer game. The concept of the game is simple, though its solving has proven to be difficult. While the main and apparent focus of the game is cooperation, it also relies on other AI-related problems such as planning, plan execution, and motion control, all connected to situational awareness. All of these must be solved in real-time. In this paper, we discuss the competition and the challenges it brings, and present an overview of the current solutions.
Prediction of daily maximum ozone levels using Lasso sparse modeling method
Venue: arXiv cs.LG 2020
Year: 2020

This paper applies modern statistical methods in the prediction of the next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone concentration of the next day. The model uses a large number of candidate features, including the present day's hourly concentration level of various pollutants, as well as the meteorological variables of the present day's observation and the future day's forecast values. In order to solve such an ultra-high dimensional problem, the least absolute shrinkage and selection operator (Lasso) was applied. The $L_1$ nature of this methodology enables the automatic feature dimension reduction, and a resultant sparse model. The model trained by 3-years data demonstrates relatively good prediction accuracy, with RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum $O_3$ concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's maximum 8-hour-mean $O_3$ concentration. Our modeling approach is also compared with several other methods recently applied in the field and demonstrates superiority in the prediction accuracy.
Optimizing Memory Performance of Xilinx FPGAs under Vitis
Venue: arXiv cs.DC 2020
Year: 2020

Plenty of research efforts have been devoted to FPGA-based acceleration, due to its low latency and high energy efficiency. However, using the original low-level hardware description languages like Verilog to program FPGAs requires generally good knowledge of hardware design details and hand-on experiences. Fortunately, the FPGA community intends to address this low programmability issues. For example, , with the intention that programming FPGAs is just as easy as programming GPUs. Even though Vitis is proven to increase programmability, we cannot directly obtain high performance without careful design regarding hardware pipeline and memory subsystem.In this paper, we focus on the memory subsystem, comprehensively and systematically benchmarking the effect of optimization methods on memory performance. Upon benchmarking, we quantitatively analyze the typical memory access patterns for a broad range of applications, including AI, HPC, and database. Further, we also provide the corresponding optimization direction for each memory access pattern so as to improve overall performance.
Dynamic Ensemble Learning for Credit Scoring: A Comparative Study
Venue: arXiv cs.LG 2020
Year: 2020

Automatic credit scoring, which assesses the probability of default by loan applicants, plays a vital role in peer-to-peer lending platforms to reduce the risk of lenders. Although it has been demonstrated that dynamic selection techniques are effective for classification tasks, the performance of these techniques for credit scoring has not yet been determined. This study attempts to benchmark different dynamic selection approaches systematically for ensemble learning models to accurately estimate the credit scoring task on a large and high-dimensional real-life credit scoring data set. The results of this study indicate that dynamic selection techniques are able to boost the performance of ensemble models, especially in imbalanced training environments.
Max-Min Power Control in Downlink Massive MIMO with Distributed Antenna Arrays
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a networkwide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA massive MIMO. For some specific scenarios, DAA massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA massive MIMO.