OpenRaiser/Intern-Atlas huggingface dataset:
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining
Venue: arXiv cs.CL 2019
Year: 2019

With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task. Although there are many available metrics that rank companies, there is an inherent need for a generalized metric that takes into account the different aspects that constitute employee opinions of the companies. In this work, we aim to overcome the aforementioned problem by generating aspect-sentiment based embedding for the companies by looking into reliable employee reviews of them. We created a comprehensive dataset of company reviews from the famous website Glassdoor.com and employed a novel ensemble approach to perform aspect-level sentiment analysis. Although a relevant amount of work has been done on reviews centered on subjects like movies, music, etc., this work is the first of its kind. We also provide several insights from the collated embeddings, thus helping users gain a better understanding of their options as well as select companies using customized preferences.
A Distributed Multi-RF Chain Hybrid mmWave Scheme for Small-cell Systems
Venue: arXiv cs.IT 2019
Year: 2019

This paper proposes a distributed hybrid millimeter wave (mmWave) scheme to exploit the structure of a Densely Deployed Distributed (DDD) small-cell-base-stations (SBSs) system for serving multiple users in a geographic area. Both the SBSs and the users are equipped with full access hybrid architectures with multi-antenna arrays and multiple radio frequency chains. Unlike the conventional cellular networks where users receive data streams from their nearest BSs, the users in our proposed scheme simultaneously receive data streams from different SBSs. With appropriate design of analog beamformers, co-channel multi-data-stream interference can be mitigated and the extra spatial degrees of freedom induced by the geographic distributed SBSs are exploited for data multiplexing. Analytical and simulation results show that the proposed scheme can improve the system sum-rate considerably, especially when the number of scattering components in millimeter wave channels is limited.
Exploitation Techniques and Defenses for Data-Oriented Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Data-oriented attacks manipulate non-control data to alter a program's benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this SoK paper, we first map data-oriented exploits, including Data-Oriented Programming (DOP) attacks, to their assumptions/requirements and attack capabilities. We also compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. Then, we experimentally assess the feasibility of a detection approach that is based on the Intel Processor Trace (PT) technology. PT only traces control flows, thus, is generally believed to be not useful for data-oriented security. However, our work reveals that data-oriented attacks (in particular the recent DOP attacks) may generate side-effects on control-flow behavior in multiple dimensions, which manifest in PT traces. Based on this evaluation, we discuss challenges for building deployable data-oriented defenses and open research questions.
On Transforming Narrowing Trees into Regular Tree Grammars Generating Ranges of Substitutions
Venue: arXiv cs.LO 2019
Year: 2019

The grammar representation of a narrowing tree for a syntactically deterministic conditional term rewriting system and a pair of terms is a regular tree grammar that generates expressions for substitutions obtained by all possible innermost-narrowing derivations that start with the pair and end with particular non-narrowable terms. In this paper, under a certain syntactic condition, we show a transformation of the grammar representation of a narrowing tree into another regular tree grammar that overapproximately generates the ranges of ground substitutions generated by the grammar representation. In our previous work, such a transformation is restricted to the ranges w.r.t. a given single variable, and thus, the usefulness is limited. We extend the previous transformation by representing the range of a ground substitution as a tuple of terms, which is obtained by the coding for finite trees. We show a precise definition of the transformation and prove that the language of the transformed regular tree grammar is an overapproximation of the ranges of ground substitutions generated by the grammar representation. We leave an experiment to evaluate the usefulness of the transformation as future work.
DVFS as a Security Failure of TrustZone-enabled Heterogeneous SoC
Venue: arXiv cs.CR 2019
Year: 2019

Today, most embedded systems use Dynamic Voltage and Frequency Scaling (DVFS) to minimize energy consumption and maximize performance. The DVFS technique works by regulating the important parameters that govern the amount of energy consumed in a system, voltage and frequency. For the implementation of this technique, the operating system (OS) includes software applications that dynamically control a voltage regulator or a frequency regulator or both. In this paper, we demonstrate for the first time a malicious use of the frequency regulator against a TrustZone-enabled System-on-Chip (SoC). We use frequency scaling to create a covert channel in a TrustZone-enabled heterogeneous SoC. We present three different attacks, the first is discreet transmission of sensitive data from the SoC to outside, using electromagnetic emission. The second attack is the inside-SoC transfer of valuable data from a secure ARM core to a non-secure one. The last attack is the inside-SoC transfer of data between a non-trusted third party IP embedded in the programmable logic part of the SoC and a processor core.
Coverage Performance of Aerial-Terrestrial HetNets
Venue: arXiv cs.IT 2019
Year: 2019

Providing seamless coverage under current cellular network technologies is surmountable only through gross overengineering. Alternatively, as an economically effective solution, the use of unmanned aerial vehicles (UAVs), augmented with the functionalities of terrestrial base stations (BSs), is recently advocated. In this paper we investigate the effect that the incorporation of UAV-mounted BSs (U-BS) poses on the coverage probability of cellular networks. To this end, we focus on the evaluation of the coverage probability of a large-scale aerialterrestrial heterogenous cellular network (AT-HetNet), in which BSs of each technology/tier can be either ground (G-BS) or UBS. Our analysis incorporates the impact of Line-of-Sight (LOS) and non-LOS (NLOS) path-loss attenuations of both ground-toground (G2G) and Air-to-Ground (A2G) links. Adopting tools of stochastic geometry we provide an expression for the coverage probability based on main system parameters and percentage of BSs in each tier that are aerial. We confirm the accuracy of our analysis. Using our analysis, we observe that for several common communication environments, e.g., high-rise and dense urban environments, the inclusion of U-BSs can be detrimental to the coverage probability. Nevertheless, it is still possible to minimize the coverage cost by turning off a percentage of G-BSs. Interestingly, for urban and sub-urban areas one can adjust the altitude of U-BSs in order to increase the coverage probability.
Parameterized k-Clustering: The distance matters!
Venue: arXiv cs.DS 2019
Year: 2019

We consider the $k$-Clustering problem, which is for a given multiset of $n$ vectors $X\subset \mathbb{Z}^d$ and a nonnegative number $D$, to decide whether $X$ can be partitioned into $k$ clusters $C_1, \dots, C_k$ such that the cost \[\sum_{i=1}^k \min_{c_i\in \mathbb{R}^d}\sum_{x \in C_i} \|x-c_i\|_p^p \leq D,\] where $\|\cdot\|_p$ is the Minkowski ($L_p$) norm of order $p$. For $p=1$, $k$-Clustering is the well-known $k$-Median. For $p=2$, the case of the Euclidean distance, $k$-Clustering is $k$-Means. We show that the parameterized complexity of $k$-Clustering strongly depends on the distance order $p$. In particular, we prove that for every $p\in (0,1]$, $k$-Clustering is solvable in time $2^{O(D \log{D})} (nd)^{O(1)}$, and hence is fixed-parameter tractable when parameterized by $D$. On the other hand, we prove that for distances of orders $p=0$ and $p=\infty$, no such algorithm exists, unless FPT=W[1].
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations
Venue: arXiv cs.LG 2019
Year: 2019

This paper gives a review and synthesis of methods of evaluating dimensionality reduction techniques. Particular attention is paid to rank-order neighborhood evaluation metrics. A framework is created for exploring dimensionality reduction quality through visualization. An associated toolkit is implemented in R. The toolkit includes scatter plots, heat maps, loess smoothing, and performance lift diagrams. The overall rationale is to help researchers compare dimensionality reduction techniques and use visual insights to help select and improve techniques. Examples are given for dimensionality reduction of manifolds and for the dimensionality reduction applied to a consumer survey dataset.
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes
Venue: arXiv cs.LG 2019
Year: 2019

A survey is performed of various Multi-Armed Bandit (MAB) strategies in order to examine their performance in circumstances exhibiting non-stationary stochastic reward functions in conjunction with delayed feedback. We run several MAB simulations to simulate an online eCommerce platform for grocery pick up, optimizing for product availability. In this work, we evaluate several popular MAB strategies, such as $ε$-greedy, UCB1, and Thompson Sampling. We compare the respective performances of each MAB strategy in the context of regret minimization. We run the analysis in the scenario where the reward function is non-stationary. Furthermore, the process experiences delayed feedback, where the reward function is not immediately responsive to the arm played. We devise a new adaptive technique (AG1) tailored for non-stationary reward functions in the delayed feedback scenario. The results of the simulation show show superior performance in the context of regret minimization compared to traditional MAB strategies.
Drug-drug interaction prediction based on co-medication patterns and graph matching
Venue: arXiv cs.LG 2019
Year: 2019

Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary orders are developed within support vector machines for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. Results: The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. Conclusions: The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest. Keywords: drug-drug interaction prediction; drug combination similarity; co-medication; graph matching
$\ell_1$-sparsity Approximation Bounds for Packing Integer Programs
Venue: arXiv cs.DS 2019
Year: 2019

We consider approximation algorithms for packing integer programs (PIPs) of the form $\max\{\langle c, x\rangle : Ax \le b, x \in \{0,1\}^n\}$ where $c$, $A$, and $b$ are nonnegative. We let $W = \min_{i,j} b_i / A_{i,j}$ denote the width of $A$ which is at least $1$. Previous work by Bansal et al. \cite{bansal-sparse} obtained an $Ω(\frac{1}{Δ_0^{1/\lfloor W \rfloor}})$-approximation ratio where $Δ_0$ is the maximum number of nonzeroes in any column of $A$ (in other words the $\ell_0$-column sparsity of $A$). They raised the question of obtaining approximation ratios based on the $\ell_1$-column sparsity of $A$ (denoted by $Δ_1$) which can be much smaller than $Δ_0$. Motivated by recent work on covering integer programs (CIPs) \cite{cq,chs-16} we show that simple algorithms based on randomized rounding followed by alteration, similar to those of Bansal et al. \cite{bansal-sparse} (but with a twist), yield approximation ratios for PIPs based on $Δ_1$. First, following an integrality gap example from \cite{bansal-sparse}, we observe that the case of $W=1$ is as hard as maximum independent set even when $Δ_1 \le 2$. In sharp contrast to this negative result, as soon as width is strictly larger than one, we obtain positive results via the natural LP relaxation. For PIPs with width $W = 1 + ε$ where $ε\in (0,1]$, we obtain an $Ω(ε^2/Δ_1)$-approximation. In the large width regime, when $W \ge 2$, we obtain an $Ω((\frac{1}{1 + Δ_1/W})^{1/(W-1)})$-approximation. We also obtain a $(1-ε)$-approximation when $W = Ω(\frac{\log (Δ_1/ε)}{ε^2})$.
A General Framework for Structured Learning of Mechanical Systems
Venue: arXiv cs.RO 2019
Year: 2019

Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high variance. We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not. We propose to parameterize a mechanical system using neural networks to model its Lagrangian and the generalized forces that act on it. We test our method on a simulated, actuated double pendulum. We show that our method outperforms a naive, black-box model in terms of data-efficiency, as well as performance in model-based reinforcement learning. We also conduct a systematic study of our method's ability to incorporate available prior knowledge about the system to improve data efficiency.
Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer
Venue: arXiv cs.CV 2019
Year: 2019

Face recognition in real life situations like low illumination condition is still an open challenge in biometric security. It is well established that the state-of-the-art methods in face recognition provide low accuracy in the case of poor illumination. In this work, we propose an algorithm for a more robust illumination invariant face recognition using a multi-modal approach. We propose a new dataset consisting of aligned faces of thermal and visual images of a hundred subjects. We then apply face detection on thermal images using the biggest blob extraction method and apply them for fusing images of different modalities for the purpose of face recognition. An algorithm is proposed to implement fusion of thermal and visual images. We reason for why relying on only one modality can give erroneous results. We use a lighter and faster CNN model called MobileNet for the purpose of face recognition with faster inferencing and to be able to be use it in real time biometric systems. We test our proposed method on our own created dataset to show that real-time face recognition on fused images shows far better results than using visual or thermal images separately.
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
Venue: arXiv cs.CL 2019
Year: 2019

Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge. Categories exist across cultures: they allow to efficiently represent the complexity of the world, and members of a community strongly agree on their nature, revealing a shared mental representation. Models of category learning and representation, however, are typically tested on data from small-scale experiments involving small sets of concepts with artificially restricted features; and experiments predominantly involve participants of selected cultural and socio-economical groups (very often involving western native speakers of English such as U.S. college students) . This work investigates whether models of categorization generalize (a) to rich and noisy data approximating the environment humans live in; and (b) across languages and cultures. We present a Bayesian cognitive model designed to jointly learn categories and their structured representation from natural language text which allows us to (a) evaluate performance on a large scale, and (b) apply our model to a diverse set of languages. We show that meaningful categories comprising hundreds of concepts and richly structured featural representations emerge across languages. Our work illustrates the potential of recent advances in computational modeling and large scale naturalistic datasets for cognitive science research.
An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures
Venue: arXiv cs.RO 2019
Year: 2019

Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a Traveling Salesman Problem (TSP), which is NP-hard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5\% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.
Privacy Preserving Location Data Publishing: A Machine Learning Approach
Venue: arXiv cs.LG 2019
Year: 2019

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose. A variation of $k$-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.
Secret Key Capacity For Multipleaccess Channel With Public Feedback
Venue: arXiv cs.IT 2019
Year: 2019

We consider the generation of a secret key (SK) by the inputs and the output of a secure multipleaccess channel (MAC) that additionally have access to a noiseless public communication channel. Under specific restrictions on the protocols, we derive various upper bounds on the rate of such SKs. Specifically, if the public communication consists of only the feedback from the output terminal, then the rate of SKs that can be generated is bounded above by the maximum symmetric rate $R_f^\ast$ in the capacity region of the MAC with feedback. On the other hand, if the public communication is allowed only before and after the transmission over the MAC, then the rate of SKs is bounded above by the maximum symmetric rate $R^\ast$ in the capacity region of the MAC without feedback. Furthermore, for a symmetric MAC, we present a scheme that generates an SK of rate $R_f^\ast$, improving the best previously known achievable rate $R^\ast$. An application of our results establishes the SK capacity for adder MAC, without any restriction on the protocols.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
Venue: arXiv cs.LG 2019
Year: 2019

A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper-parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with shifted distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results show that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of dominated hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates.
Designing for Health Chatbots
Venue: arXiv cs.HC 2019
Year: 2019

Building conversational agents have many technical, design and linguistic challenges. Other more complex elements include using emotionally intelligent conversational agent to build trust with the individuals. In this chapter, we introduce the nature of conversational user interfaces (CUIs) for health and describe UX design principles informed by a systematic literature review of relevant research works. We analyze scientific literature in conversational interfaces and chatterbots, providing a survey of major studies and describing UX design principles and interaction patterns.