OpenRaiser/Intern-Atlas huggingface dataset:
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
Venue: arXiv cs.HC 2019
Year: 2019

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain.
Predicting Personality Traits from Physical Activity Intensity
Venue: arXiv cs.HC 2019
Year: 2019

Call and messaging logs from mobile devices have been used to predict human personality traits successfully in recent years. However, the widely available accelerometer data is not yet utilized for this purpose. In this research, we explored some important features describing human physical activity intensity, used for the very first time to predict human personality traits through raw accelerometer data. Using a set of newly introduced metrics, we combined physical activity intensity features with traditional phone activity features for personality prediction. The experiment results show that the predicted personality scores are closer to the ground truth, with observable reduction of errors in predicting the Big-5 personality traits across male and female.
Space Efficient Algorithms for Breadth-Depth Search
Venue: arXiv cs.DS 2019
Year: 2019

Continuing the recent trend, in this article we design several space-efficient algorithms for two well-known graph search methods. Both these search methods share the same name {\it breadth-depth search} (henceforth {\sf BDS}), although they work entirely in different fashion. The classical implementation for these graph search methods takes $O(m+n)$ time and $O(n \lg n)$ bits of space in the standard word RAM model (with word size being $Θ(\lg n)$ bits), where $m$ and $n$ denotes the number of edges and vertices of the input graph respectively. Our goal here is to beat the space bound of the classical implementations, and design $o(n \lg n)$ space algorithms for these search methods by paying little to no penalty in the running time. Note that our space bounds (i.e., with $o(n \lg n)$ bits of space) do not even allow us to explicitly store the required information to implement the classical algorithms, yet our algorithms visits and reports all the vertices of the input graph in correct order.
Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study
Venue: arXiv cs.CY 2019
Year: 2019

Progress in the field of artificial intelligence has been accelerating rapidly in the past two decades. Various autonomous systems from purely digital ones to autonomous vehicles are being developed and deployed out on the field. As these systems exert a growing impact on society, ethics in relation to artificial intelligence and autonomous systems have recently seen growing attention among the academia. However, the current literature on the topic has focused almost exclusively on theory and more specifically on conceptualization in the area. To widen the body of knowledge in the area, we conduct an empirical study on the current state of practice in artificial intelligence ethics. We do so by means of a multiple case study of five case companies, the results of which indicate a gap between research and practice in the area. Based on our findings we propose ways to tackle the gap.
Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we propose two novel methods for domain adaptation for the attention-only neural machine translation (NMT) model, i.e., the Transformer. Our methods focus on training a single translation model for multiple domains by either learning domain specialized hidden state representations or predictor biases for each domain. We combine our methods with a previously proposed black-box method called mixed fine tuning, which is known to be highly effective for domain adaptation. In addition, we incorporate multilingualism into the domain adaptation framework. Experiments show that multilingual multi-domain adaptation can significantly improve both resource-poor in-domain and resource-rich out-of-domain translations, and the combination of our methods with mixed fine tuning achieves the best performance.
Accurate decoding of materials using a finger mounted accelerometer
Venue: arXiv cs.HC 2019
Year: 2019

Sensory feedback is the fundamental driving force behind motor control and learning. However, the technology for low-cost and efficient sensory feedback remains a big challenge during stroke rehabilitation, and for prosthetic designs. Here we show that a low-cost accelerometer mounted on the finger can provide accurate decoding of many daily life materials during touch. We first designed a customized touch analysis system that allowed us to present different materials for touch by human participants, while controlling for the contact force and touch speed. Then, we collected data from six participants, who touched seven daily life materials-plastic, cork, wool, aluminum, paper, denim, cotton. We use linear sparse logistic regression and show that the materials can be classified from accelerometer recordings with an accuracy of 88% across materials and participants within 7 seconds of touch.
On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs
Venue: arXiv cs.NI 2019
Year: 2019

The Spatial Reuse (SR) operation included in the IEEE 802.11ax-2020 (11ax) amendment aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS). However, many unknowns exist about the performance gains that can be achieved through SR. In this paper, we provide a brief introduction to the SR operation described in the IEEE 802.11ax (draft D4.0). Then, a simulation-based implementation is provided in order to explore the performance gains of the SR operation. Our results show the potential of using SR in different scenarios covering multiple network densities and traffic loads. In particular, we observe significant improvements on the channel utilization when applying SR with respect to the default configuration, thus allowing to increase the throughput and reduce the delay. Interestingly, the highest improvements provided by the SR operation are observed in the most pessimistic situations in terms of network density and traffic load.
Online Heterogeneous Mixture Learning for Big Data
Venue: arXiv cs.LG 2019
Year: 2019

We propose the online machine learning for big data analysis with heterogeneity. We performed an experiment to compare the accuracy of each iteration between batch one and online one. It is possible to converge quickly with the same accuracy as the batch one.
Growth and Duplication of Public Source Code over Time: Provenance Tracking at Scale
Venue: arXiv cs.SE 2019
Year: 2019

We study the evolution of the largest known corpus of publicly available source code, i.e., the Software Heritage archive (4B unique source code files, 1B commits capturing their development histories across 50M software projects). On such corpus we quantify the growth rate of original, never-seen-before source code files and commits. We find the growth rates to be exponential over a period of more than 40 years.We then estimate the multiplication factor, i.e., how much the same artifacts (e.g., files or commits) appear in different contexts (e.g., commits or source code distribution places). We observe a combinatorial explosion in the multiplication of identical source code files across different commits.We discuss the implication of these findings for the problem of tracking the provenance of source code artifacts (e.g., where and when a given source code file or commit has been observed in the wild) for the entire body of publicly available source code. To that end we benchmark different data models for capturing software provenance information at this scale and growth rate. We identify a viable solution that is deployable on commodity hardware and appears to be maintainable for the foreseeable future.
Automatic Source Code Summarization with Extended Tree-LSTM
Venue: arXiv cs.LG 2019
Year: 2019

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short-Term Memory (LSTM), can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially {\em structured}, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code. Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree-LSTM is proposed as a generalization of LSTMs for tree-structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree-LSTM, which we call \emph{Multi-way Tree-LSTM} and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state-of-the-art techniques.
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
Venue: arXiv cs.LG 2019
Year: 2019

We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Markovian dynamics. The reward state of passive arms (which are not chosen to play at time t) evolves according to an arbitrary unknown random process. The objective is an arm-selection policy that minimizes the regret, defined as the reward loss with respect to a player that always plays the most rewarding arm. This class of RMAB problems has been studied recently in the context of communication networks and financial investment applications. We develop a strategy that selects arms to be played in a consecutive manner, dubbed Adaptive Sequencing Rules (ASR) algorithm. The sequencing rules for selecting arms under the ASR algorithm are adaptively updated and controlled by the current sample reward means. By designing judiciously the adaptive sequencing rules, we show that the ASR algorithm achieves a logarithmic regret order with time, and a finite-sample bound on the regret is established. Although existing methods have shown a logarithmic regret order with time in this RMAB setting, the theoretical analysis shows a significant improvement in the regret scaling with respect to the system parameters under ASR. Extensive simulation results support the theoretical study and demonstrate strong performance of the algorithm as compared to existing methods.
Does Diversity Improve the Test Suite Generation for Mobile Applications?
Venue: arXiv cs.SE 2019
Year: 2019

In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trial-and-error basis, which may lead to better results for a specific problem. To obtain better results while avoiding trial-and-error experiments, a fitness landscape analysis is helpful in understanding the search problem, and making an informed decision about the heuristics. In this paper, we investigate the search problem of test suite generation for mobile applications (apps) using SAPIENZ whose heuristic is a default NSGA-II. We analyze the fitness landscape of SAPIENZ with respect to genotypic diversity and use the gained insights to adapt the heuristic of SAPIENZ. These adaptations result in SAPIENZ^div that aims for preserving the diversity of test suites during the search. To evaluate SAPIENZ^div, we perform a head-to-head comparison with SAPIENZ on 76 open-source apps.
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design
Venue: arXiv cs.LG 2019
Year: 2019

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each parameter in the search space, manually tuning of DNN hyperparameters is impractical. Automatic joint DNN and hardware hyperparameter optimization is indispensable for such problems. Bayesian optimization-based approaches have shown promising results for hyperparameter optimization of DNNs. However, most of these techniques have been developed without considering the underlying hardware, thereby leading to inefficient designs. Further, the few works that perform joint optimization are not generalizable and mainly focus on CMOS-based architectures. In this work, we present a novel pseudo agent-based multi-objective hyperparameter optimization (PABO) for maximizing the DNN performance while obtaining low hardware cost. Compared to the existing methods, our work poses a theoretically different approach for joint optimization of accuracy and hardware cost and focuses on memristive crossbar-based accelerators. PABO uses a supervisor agent to establish connections between the posterior Gaussian distribution models of network accuracy and hardware cost requirements. The agent reduces the mathematical complexity of the co-optimization problem by removing unnecessary computations and updates of acquisition functions, thereby achieving significant speed-ups for the optimization procedure. PABO outputs a Pareto frontier that underscores the trade-offs between designing high-accuracy and hardware efficiency. Our results demonstrate a superior performance compared to the state-of-the-art methods both in terms of accuracy and computational speed (~100x speed up).
Strategy Representation by Decision Trees with Linear Classifiers
Venue: arXiv cs.LO 2019
Year: 2019

Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of $ω$-regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees.
Predicting Patent Citations to measure Economic Impact of Scholarly Research
Venue: arXiv cs.DL 2019
Year: 2019

A crucial goal of funding research and development has always been to advance economic development. On this basis, a consider-able body of research undertaken with the purpose of determining what exactly constitutes economic impact and how to accurately measure that impact has been published. Numerous indicators have been used to measure economic impact, although no single indicator has been widely adapted. Based on patent data collected from Altmetric we predict patent citations through various social media features using several classification models. Patents citing a research paper implies the potential it has for direct application inits field. These predictions can be utilized by researchers in deter-mining the practical applications for their work when applying for patents.
Mutually Orthogonal Latin Squares based on Cellular Automata
Venue: arXiv cs.DM 2019
Year: 2019

We investigate sets of Mutually Orthogonal Latin Squares (MOLS) generated by Cellular Automata (CA) over finite fields. After introducing how a CA defined by a bipermutive local rule of diameter $d$ over an alphabet of $q$ elements generates a Latin square of order $q^{d-1}$, we study the conditions under which two CA generate a pair of orthogonal Latin squares. In particular, we prove that the Latin squares induced by two Linear Bipermutive CA (LBCA) over the finite field $\mathbb{F}_q$ are orthogonal if and only if the polynomials associated to their local rules are relatively prime. Next, we enumerate all such pairs of orthogonal Latin squares by counting the pairs of coprime monic polynomials with nonzero constant term and degree $n$ over $\mathbb{F}_q$. Finally, we present a construction of MOLS generated by LBCA with irreducible polynomials and prove the maximality of the resulting sets, as well as a lower bound which is asymptotically close to their actual number.
Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing approaches, we not only learn the control policy, but also a "certificate policy", that allows us to estimate the sub-optimality of the learned control policy online, during execution-time. We learn both these policies from data using supervised learning techniques, and also provide a randomized method that allows us to guarantee the quality of each learned policy, measured in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned control policy of being infeasible or suboptimal, where the check is performed by the certificate policy. Since our algorithm does not require the solution of an optimization problem during run-time, it can be deployed even on resource-constrained systems. We illustrate the efficacy of the proposed framework on a vehicle dynamics control problem where we demonstrate a speedup of up to two orders of magnitude compared to online optimization with minimal performance degradation.
Considerations for the Interpretation of Bias Measures of Word Embeddings
Venue: arXiv cs.CL 2019
Year: 2019

Word embedding spaces are powerful tools for capturing latent semantic relationships between terms in corpora, and have become widely popular for building state-of-the-art natural language processing algorithms. However, studies have shown that societal biases present in text corpora may be incorporated into the word embedding spaces learned from them. Thus, there is an ethical concern that human-like biases contained in the corpora and their derived embedding spaces might be propagated, or even amplified with the usage of the biased embedding spaces in downstream applications. In an attempt to quantify these biases so that they may be better understood and studied, several bias metrics have been proposed. We explore the statistical properties of these proposed measures in the context of their cited applications as well as their supposed utilities. We find that there are caveats to the simple interpretation of these metrics as proposed. We find that the bias metric proposed by Bolukbasi et al. 2016 is highly sensitive to embedding hyper-parameter selection, and that in many cases, the variance due to the selection of some hyper-parameters is greater than the variance in the metric due to corpus selection, while in fewer cases the bias rankings of corpora vary with hyper-parameter selection. In light of these observations, it may be the case that bias estimates should not be thought to directly measure the properties of the underlying corpus, but rather the properties of the specific embedding spaces in question, particularly in the context of hyper-parameter selections used to generate them. Hence, bias metrics of spaces generated with differing hyper-parameters should be compared only with explicit consideration of the embedding-learning algorithms particular configurations.
2D Linear Time-Variant Controller for Human's Intention Detection for Reach-to-Grasp Trajectories in Novel Scenes
Venue: arXiv cs.RO 2019
Year: 2019

Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human operated manipulators or exoskeletons. The key insight is to develop a system that takes into account context- and user-awareness to take better decisions in how to assist the user. The context-awareness is implemented by enabling the system to automatically generate a set of candidate grasps and reach-to-grasp trajectories in novel, cluttered scenes. The user-awareness is implemented as a linear time-variant feedback controller to facilitate the motion towards the most promising grasp. Our approach is demonstrated in a simple 2D example in which participants are asked to grasp a specific object in a clutter scene. Our approach also reduce the number of controllable dimensions for the user by providing only control on x- and y-axis, while orientation of the end-effector and the pose of its fingers are inferred by the system. The experimental results show the benefits of our approach in terms of accuracy and execution time with respect to a pure manual control.