OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Venue: arXiv cs.LG 2018
Year: 2018

Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagnostic tool, a consistent theory on their invertibility is still lacking. We derive a theoretically motivated approach to explore the preimages of ReLU-layers and mechanisms affecting the stability of the inverse. Using the developed theory, we numerically show how this approach uncovers characteristic properties of the network.
Understanding End-to-End Effects of Channel Dynamics in Millimeter Wave 5G New Radio
Venue: arXiv cs.IT 2018
Year: 2018

A critical challenge for wireless communications in the millimeter wave (mmWave) bands is blockage. MmWave signals suffer significant penetration losses from many common materials and objects, and small changes in the position of obstacles in the environment can cause large variations in the channel quality. This paper provides a measurement-based study of the effects of human blockage on an end-to-end application over a mmWave cellular link. A phased array system is used to measure the channel in multiple directions almost simultaneously in a realistic indoor scenario. The measurements are integrated into a detailed ns-3 simulation that models both the latest 3GPP New Radio beam search procedure as well as the internet protocol stack. The measurement-based simulation illustrates how recovery from blockage depends on the path diversity and beam search.
AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models
Venue: arXiv cs.SE 2018
Year: 2018

The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated to defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation. Since correlation analyses often involve manual selection of metrics by a domain expert, we introduce AutoSpearman, an automated metric selection approach based on correlation analyses. Our evaluation indicates that AutoSpearman yields the highest consistency of subsets of metrics among training samples and mitigates correlated metrics, while impacting model performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when interpreting defect models, we recommend future studies use AutoSpearman in lieu of commonly-used feature selection techniques.
A NoSQL Data-based Personalized Recommendation System for C2C e-Commerce
Venue: arXiv cs.IR 2018
Year: 2018

With the considerable development of customer-to-customer (C2C) e-commerce in the recent years, there is a big demand for an effective recommendation system that suggests suitable websites for users to sell their items with some specified needs. Nonetheless, e-commerce recommendation systems are mostly designed for business-to-customer (B2C) websites, where the systems offer the consumers the products that they might like to buy. Almost none of the related research works focus on choosing selling sites for target items. In this paper, we introduce an approach that recommends the selling websites based upon the item's description, category, and desired selling price. This approach employs NoSQL data-based machine learning techniques for building and training topic models and classification models. The trained models can then be used to rank the websites dynamically with respect to the user needs. The experimental results with real-world datasets from Vietnam C2C websites will demonstrate the effectiveness of our proposed method.
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Venue: arXiv cs.CV 2018
Year: 2018

Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them. In the domain of clothing recommendation, incorporating items' visual information (e.g., product images) is particularly important since clothing item appearance is often a critical factor in influencing the user's purchasing decisions. Current state-of-the-art visually-aware recommender systems utilize image features extracted from pre-trained deep convolutional neural networks, however these extremely high-dimensional representations are difficult to interpret, especially in relation to the relatively low number of visual properties that may guide users' decisions. In this paper we propose a novel approach to personalized clothing recommendation that models the dynamics of individual users' visual preferences. By using interpretable image representations generated with a unique feature learning process, our model learns to explain users' prior feedback in terms of their affinity towards specific visual attributes and styles. Our approach achieves state-of-the-art performance on personalized ranking tasks, and the incorporation of interpretable visual features allows for powerful model introspection, which we demonstrate by using an interactive recommendation algorithm and visualizing the rise and fall of fashion trends over time.
SENSE: Abstraction-Based Synthesis of Networked Control Systems
Venue: arXiv cs.SY 2018
Year: 2018

While many studies and tools target the basic stabilizability problem of networked control systems (NCS), nowadays modern systems require more sophisticated objectives such as those expressed as formulae in linear temporal logic or as automata on infinite strings. One general technique to achieve this is based on so-called symbolic models, where complex systems are approximated by finite abstractions, and then, correct-by-construction controllers are automatically synthesized for them. We present tool SENSE for the construction of finite abstractions for NCS and the automated synthesis of controllers. Constructed controllers enforce complex specifications over plants in NCS by taking into account several non-idealities of the communication channels. Given a symbolic model of the plant and network parameters, SENSE can efficiently construct a symbolic model of the NCS, by employing operations on binary decision diagrams (BDDs). Then, it synthesizes symbolic controllers satisfying a class of specifications. It has interfaces for the simulation and the visualization of the resulting closed-loop systems using OMNETPP and MATLAB. Additionally, SENSE can generate ready-to-implement VHDL/Verilog or C/C++ codes from the synthesized controllers.
Charge-then-Forward: Wireless Powered Communication for Multiuser Relay Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies a relay-assisted wireless powered communication network (R-WPCN) consisting of multiple source-destination pairs and a hybrid relay node (HRN). We consider a "charge-then-forward" protocol at the HRN, in which the HRN with constant energy supply first acts as an energy transmitter to charge the sources, and then forwards the information from the sources to their destinations through time division multiple access (TDMA) or frequency division multiple access (FDMA). Processing costs at the wireless-powered sources are taken into account. Our goal is to maximize the sum-rate of all transmission pairs by jointly optimizing the time, frequency and power resources. The formulated optimization problems for both TDMA and FDMA are non-convex. For the TDMA scheme, by appropriate transformation, the problem is reformulated as a convex problem and be optimally solved. For the FDMA case, we find the asymptotically optimal solution in the dual domain. Furthermore, suboptimal algorithms are proposed for both schemes to tradeoff the complexity and performance. Finally, the simulation results validate the effectiveness of the proposed schemes.
An empirical investigation of the Tribes and their Territories: are research specialisms rural and urban?
Venue: arXiv cs.DL 2018
Year: 2018

We propose an operationalization of the rural and urban analogy introduced in Becher and Trowler [2001]. According to them, a specialism is rural if it is organized into many, smaller topics of research, with higher author mobility among them, lower rate of collaboration and productivity, lower competition for resources and citation recognitions compared to an urban specialism. It is assumed that most humanities specialisms are rural while science specialisms are in general urban: we set to test this hypothesis empirically. We first propose an operationalization of the theory in most of its quantifiable aspects. We then consider specialisms from history, literature, computer science, biology, astronomy. Our results show that specialisms in the humanities present a sensibly lower citation and textual connectivity, in agreement with their organization into more, smaller topics per specialism, as suggested by the analogy. We argue that the intellectual organization of rural specialisms might indeed be qualitative different from urban ones, discouraging the straightforward application of citation-based indicators commonly applied to urban specialisms without a dedicated re-design in acknowledgement of these differences.
Probabilistic Inference Using Generators - The Statues Algorithm
Venue: arXiv cs.AI 2018
Year: 2018

We present here a new probabilistic inference algorithm that gives exact results in the domain of discrete probability distributions. This algorithm, named the Statues algorithm, calculates the marginal probability distribution on probabilistic models defined as direct acyclic graphs. These models are made up of well-defined primitives that allow to express, in particular, joint probability distributions, Bayesian networks, discrete Markov chains, conditioning and probabilistic arithmetic. The Statues algorithm relies on a variable binding mechanism based on the generator construct, a special form of coroutine; being related to the enumeration algorithm, this new algorithm brings important improvements in terms of efficiency, which makes it valuable in regard to other exact marginalization algorithms. After introduction of several definitions, primitives and compositional rules, we present in details the Statues algorithm. Then, we briefly discuss the interest of this algorithm compared to others and we present possible extensions. Finally, we introduce Lea and MicroLea, two Python libraries implementing the Statues algorithm, along with several use cases. A proof of the correctness of the algorithm is provided in appendix.
Banach-Mazur Parity Games and Almost-sure Winning Strategies
Venue: arXiv cs.GT 2018
Year: 2018

Two-player stochastic games are games with two 2 players and a randomised entity called "nature". A natural question to ask in this framework is the existence of strategies that ensure that an event happens with probability 1 (almost-sure strategies). In the case of Markov decision processes, when the event 2 of interest is given as a parity condition, we can replace the "nature" by two more players that play according to the rules of what is known as Banach-Mazur game [1]. In this paper we continue this research program by extending the above result to two-player stochastic parity games. As in the paper [1], the basic idea is that, under the correct hypothesis, we can replace the randomised player with two players playing a Banach-Mazur game. This requires a few technical observations, and a non trivial proof, that this paper sets out to do.
AlertMix: A Big Data platform for multi-source streaming data
Venue: arXiv cs.DC 2018
Year: 2018

The demand for stream processing is increasing at an unprecedented rate. Big data is no longer limited to processing of big volumes of data. In most real-world scenarios, the need for processing stream data as it comes can only meet the business needs. It is required for trading, fraud detection, system monitoring, product maintenance and of course social media data such as Twitter and YouTube videos. In such cases, a "too late architecture" that focuses on batch processing cannot realize the use cases. In this article, we present an end to end Big data platform called AlertMix for processing multi-source streaming data. Its architecture and how various Big data technologies are utilized are explained in this work. We present the performance of our platform on real live streaming data which is currently handled by the platform.
A General Framework for Anytime Approximation in Probabilistic Databases
Venue: arXiv cs.DB 2018
Year: 2018

Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks. Those approaches have been based so far on either (i) sampling or (ii) branch-and-bound with model-based bounds. We present here a more general branch-and-bound framework that extends the possible bounds by using 'dissociation', which yields tighter bounds.
A Theory of Diagnostic Interpretation in Supervised Classification
Venue: arXiv cs.LG 2018
Year: 2018

Interpretable deep learning is a fundamental building block towards safer AI, especially when the deployment possibilities of deep learning-based computer-aided medical diagnostic systems are so eminent. However, without a computational formulation of black-box interpretation, general interpretability research rely heavily on subjective bias. Clear decision structure of the medical diagnostics lets us approximate the decision process of a radiologist as a model - removed from subjective bias. We define the process of interpretation as a finite communication between a known model and a black-box model to optimally map the black box's decision process in the known model. Consequently, we define interpretability as maximal information gain over the initial uncertainty about the black-box's decision within finite communication. We relax this definition based on the observation that diagnostic interpretation is typically achieved by a process of minimal querying. We derive an algorithm to calculate diagnostic interpretability. The usual question of accuracy-interpretability tradeoff, i.e. whether a black-box model's prediction accuracy is dependent on its ability to be interpreted by a known source model, does not arise in this theory. With multiple example simulation experiments of various complexity levels, we demonstrate the working of such a theoretical model in synthetic supervised classification scenarios.
A CutFEM method for two-phase flow problems
Venue: arXiv cs.NA 2018
Year: 2018

In this article, we present a cut finite element method for two-phase Navier-Stokes flows. The main feature of the method is the formulation of a unified continuous interior penalty stabilisation approach for, on the one hand, stabilising advection and the pressure-velocity coupling and, on the other hand, stabilising the cut region. The accuracy of the algorithm is enhanced by the development of extended fictitious domains to guarantee a well defined velocity from previous time steps in the current geometry. Finally, the robustness of the moving-interface algorithm is further improved by the introduction of a curvature smoothing technique that reduces spurious velocities. The algorithm is shown to perform remarkably well for low capillary number flows, and is a first step towards flexible and robust CutFEM algorithms for the simulation of microfluidic devices.
Conditional Sparse $\ell_p$-norm Regression With Optimal Probability
Venue: arXiv cs.LG 2018
Year: 2018

We consider the following conditional linear regression problem: the task is to identify both (i) a $k$-DNF condition $c$ and (ii) a linear rule $f$ such that the probability of $c$ is (approximately) at least some given bound $μ$, and $f$ minimizes the $\ell_p$ loss of predicting the target $z$ in the distribution of examples conditioned on $c$. Thus, the task is to identify a portion of the distribution on which a linear rule can provide a good fit. Algorithms for this task are useful in cases where simple, learnable rules only accurately model portions of the distribution. The prior state-of-the-art for such algorithms could only guarantee finding a condition of probability $Ω(μ/n^k)$ when a condition of probability $μ$ exists, and achieved an $O(n^k)$-approximation to the target loss, where $n$ is the number of Boolean attributes. Here, we give efficient algorithms for solving this task with a condition $c$ that nearly matches the probability of the ideal condition, while also improving the approximation to the target loss. We also give an algorithm for finding a $k$-DNF reference class for prediction at a given query point, that obtains a sparse regression fit that has loss within $O(n^k)$ of optimal among all sparse regression parameters and sufficiently large $k$-DNF reference classes containing the query point.
The Determinants For User Intention To Adopt Web Based Early Childhood Supplementary Educational Platform
Venue: arXiv cs.CY 2018
Year: 2018

Education is defined as the fundamental key for the success and development for any given society. Hence, the forming of a strong basic educational foundation is crucial to ensure the children to stay competitive and achieve extraordinary in the changing world. I-zLink Sdn. Bhd. is a partnership company that formed by four individuals with the commitment to develop an early childhood learning solution named i-Future. The system, i-Future is a Supplementary Educational Platform that designs specially for children that fall between the age categories of two to six years which integrate with the latest technology to increase the engagement and flexibility in learning process as well as the effectiveness of study. In order to ensure that the system has a market value, an empirical study was conducted to determine the acceptance level and also the variables that would affect the user's intention to adopt i-Future. The variables used in the study include Perceived Ease of Use (PEU), Perceived Usefulness (PU), System Quality (SQ) and Social Norm (SN). Through the quantitative study, the relationship between these variables and user's intention to adopt i-Future was determined. Those variables which have significant impact on user's intention to adopt i-Future will be used to design i-Future.
Phase transition in the knapsack problem
Venue: arXiv cs.AI 2018
Year: 2018

We examine the phase transition phenomenon for the Knapsack problem from both a computational and a human perspective. We first provide, via an empirical and a theoretical analysis, a characterization of the phenomenon in terms of two instance properties; normalised capacity and normalised profit. Then, we show evidence that average time spent by human decision makers in solving an instance peaks near the phase transition. Given the ubiquity of the Knapsack problem in every-day life, a better understanding of its structure can improve our understanding not only of computational techniques but also of human behavior, including the use and development of heuristics and occurrence of biases.
Hierarchical Deep Co-segmentation of Primary Objects in Aerial Videos
Venue: arXiv cs.CV 2018
Year: 2018

Primary object segmentation plays an important role in understanding videos generated by unmanned aerial vehicles. In this paper, we propose a large-scale dataset with 500 aerial videos and manually annotated primary objects. To the best of our knowledge, it is the largest dataset to date for primary object segmentation in aerial videos. From this dataset, we find most aerial videos contain large-scale scenes, small primary objects as well as consistently varying scales and viewpoints. Inspired by that, we propose a hierarchical deep co-segmentation approach that repeatedly divides a video into two sub-videos formed by the odd and even frames, respectively. In this manner, the primary objects shared by sub-videos can be co-segmented by training two-stream CNNs and finally refined within the neighborhood reversible flows. Experimental results show that our approach remarkably outperforms 17 state-of-the-art methods in segmenting primary objects in various types of aerial videos.
The Impact of Human Factors on the Participation Decision of Reviewers in Modern Code Review
Venue: arXiv cs.SE 2018
Year: 2018

Modern Code Review (MCR) plays a key role in software quality practices. In MCR process, a new patch (i.e., a set of code changes) is encouraged to be examined by reviewers in order to identify weaknesses in source code prior to an integration into main software repositories. To mitigate the risk of having future defects, prior work suggests that MCR should be performed with sufficient review participation. Indeed, recent work shows that a low number of participated reviewers is associated with poor software quality. However, there is a likely case that a new patch still suffers from poor review participation even though reviewers were invited. Hence, in this paper, we set out to investigate the factors that are associated with the participation decision of an invited reviewer. Through a case study of 230,090 patches spread across the Android, LibreOffice, OpenStack and Qt systems, we find that (1) 16%-66% of patches have at least one invited reviewer who did not respond to the review invitation; (2) human factors play an important role in predicting whether or not an invited reviewer will participate in a review; (3) a review participation rate of an invited reviewers and code authoring experience of an invited reviewer are highly associated with the participation decision of an invited reviewer. These results can help practitioners better understand about how human factors associate with the participation decision of reviewers and serve as guidelines for inviting reviewers, leading to a better inviting decision and a better reviewer participation.
The Virtuous Machine - Old Ethics for New Technology?
Venue: arXiv cs.AI 2018
Year: 2018

Modern AI and robotic systems are characterized by a high and ever-increasing level of autonomy. At the same time, their applications in fields such as autonomous driving, service robotics and digital personal assistants move closer to humans. From the combination of both developments emerges the field of AI ethics which recognizes that the actions of autonomous machines entail moral dimensions and tries to answer the question of how we can build moral machines. In this paper we argue for taking inspiration from Aristotelian virtue ethics by showing that it forms a suitable combination with modern AI due to its focus on learning from experience. We furthermore propose that imitation learning from moral exemplars, a central concept in virtue ethics, can solve the value alignment problem. Finally, we show that an intelligent system endowed with the virtues of temperance and friendship to humans would not pose a control problem as it would not have the desire for limitless self-improvement.