OpenRaiser/Intern-Atlas huggingface dataset:
Bus transport network analysis in Rio de Janeiro based on topological models using Social Networks
Venue: arXiv cs.SI 2018
Year: 2018

In recent years, studies on public transport networks have intensified in the Social Networks field, especially in bus networks. This has occurred because of the relevance of urban mobility for the proper functioning of a city. The Rio de Janeiro city, Brazil, has undergone recent changes in the city of Rio de Janeiro, Brazil, has undergone recent changes in its municipal bus system, modifying several lines and bus stops. This paper proposes to analyze the structure of the bus transportation network of this city, comparing its topology in 2014 and 2016, before and after the change. For this, the properties of the bus system were investigated based on the topological models B\-space, P\-space, and C\-space. Some essential parameters were calculated, such as the giant component, distance, diameter, degree, closeness, and betweenness. The results showed that there was a reduction of 22.75\% of the lines and 5.19\% of the bus stops from 2014 to 2016. It was also verified that a maximum of four lines is required to move between any two bus stops within the city in both years. However, with three lines is possible to reach more than 99\% of the bus stops. Besides, this study also suggests exploring the C\-space network according to a minimum number of frequent bus stops that the lines had. Based on the component giant analysis of these networks with many common points, it is possible to detect possible expressway corridors.
Session Guarantees with Raft and Hybrid Logical Clocks
Venue: arXiv cs.DC 2018
Year: 2018

Eventual consistency is a popular consistency model for geo-replicated data stores. Although eventual consistency provides high performance and availability, it can cause anomalies that make programming complex for application developers. Session guarantees can remove some of these anomalies while causing much lower overhead compared with stronger consistency models. In this paper, we provide a protocol for providing session guarantees for NuKV, a key-value store developed for services with very high availability and performance requirements at eBay. NuKV relies on the Raft protocol for replication inside datacenters, and uses eventual consistency for replication among datacenters. We provide modified versions of conventional session guarantees to avoid the problem of slowdown cascades in systems with large numbers of partitions. We also use Hybrid Logical Clocks to eliminate the need for delaying write operations to satisfy session guarantees. Our experiments show that our protocol provides session guarantees with a negligible overhead when compared with eventual consistency.
Ensemble-based Adaptive Single-shot Multi-box Detector
Venue: arXiv cs.CV 2018
Year: 2018

We propose two improvements to the SSD---single shot multibox detector. First, we propose an adaptive approach for default box selection in SSD. This uses data to reduce the uncertainty in the selection of best aspect ratios for the default boxes and improves performance of SSD for datasets containing small and complex objects (e.g., equipments at construction sites). We do so by finding the distribution of aspect ratios of the given training dataset, and then choosing representative values. Secondly, we propose an ensemble algorithm, using SSD as components, which improves the performance of SSD, especially for small amount of training datasets. Compared to the conventional SSD algorithm, adaptive box selection improves mean average precision by 3%, while ensemble-based SSD improves it by 8%.
Efficiently Learning Mixtures of Mallows Models
Venue: arXiv cs.DS 2018
Year: 2018

Mixtures of Mallows models are a popular generative model for ranking data coming from a heterogeneous population. They have a variety of applications including social choice, recommendation systems and natural language processing. Here we give the first polynomial time algorithm for provably learning the parameters of a mixture of Mallows models with any constant number of components. Prior to our work, only the two component case had been settled. Our analysis revolves around a determinantal identity of Zagier which was proven in the context of mathematical physics, which we use to show polynomial identifiability and ultimately to construct test functions to peel off one component at a time. To complement our upper bounds, we show information-theoretic lower bounds on the sample complexity as well as lower bounds against restricted families of algorithms that make only local queries. Together, these results demonstrate various impediments to improving the dependence on the number of components. They also motivate the study of learning mixtures of Mallows models from the perspective of beyond worst-case analysis. In this direction, we show that when the scaling parameters of the Mallows models have separation, there are much faster learning algorithms.
Complete Test Sets And Their Approximations
Venue: arXiv cs.LO 2018
Year: 2018

We use testing to check if a combinational circuit $N$ always evaluates to 0 (written as $N \equiv 0$). We call a set of tests proving $N \equiv 0$ a complete test set (CTS). The conventional point of view is that to prove $N \equiv 0$ one has to generate a trivial CTS. It consists of all $2^{|X|}$ input assignments where $X$ is the set of input variables of $N$. We use the notion of a Stable Set of Assignments (SSA) to show that one can build a non-trivial CTS consisting of less than $2^{|X|}$ tests. Given an unsatisfiable CNF formula $H(W)$, an SSA of $H$ is a set of assignments to $W$ that proves unsatisfiability of $H$. A trivial SSA is the set of all $2^{|W|}$ assignments to $W$. Importantly, real-life formulas can have non-trivial SSAs that are much smaller than $2^{|W|}$. In general, construction of even non-trivial CTSs is inefficient. We describe a much more efficient approach where tests are extracted from an SSA built for a `projection' of $N$ on a subset of variables of $N$. These tests can be viewed as an approximation of a CTS for $N$. We give experimental results and describe potential applications of this approach.
Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Venue: arXiv cs.CV 2018
Year: 2018

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina dataset, called EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists.
Single-Server Multi-Message Private Information Retrieval with Side Information
Venue: arXiv cs.IT 2018
Year: 2018

We study the problem of single-server multi-message private information retrieval with side information. One user wants to recover $N$ out of $K$ independent messages which are stored at a single server. The user initially possesses a subset of $M$ messages as side information. The goal of the user is to download the $N$ demand messages while not leaking any information about the indices of these messages to the server. In this paper, we characterize the minimum number of required transmissions. We also present the optimal linear coding scheme which enables the user to download the demand messages and preserves the privacy of their indices. Moreover, we show that the trivial MDS coding scheme with $K-M$ transmissions is optimal if $N>M$ or $N^2+N \ge K-M$. This means if one wishes to privately download more than the square-root of the number of files in the database, then one must effectively download the full database (minus the side information), irrespective of the amount of side information one has available.
Reverse Flooding: exploiting radio interference for efficient propagation delay compensation in WSN clock synchronization
Venue: arXiv cs.NI 2018
Year: 2018

Clock synchronization is a necessary component in modern distributed systems, especially Wirless Sensor Networks (WSNs). Despite the great effort and the numerous improvements, the existing synchronization schemes do not yet address the cancellation of propagation delays. Up to a few years ago, this was not perceived as a problem, because the time-stamping precision was a more limiting factor for the accuracy achievable with a synchronization scheme. However, the recent introduction of efficient flooding schemes based on constructive interference has greatly improved the achievable accuracy, to the point where propagation delays can effectively become the main source of error. In this paper, we propose a method to estimate and compensate for the network propagation delays. Our proposal does not require to maintain a spanning tree of the network, and exploits constructive interference even to transmit packets whose content are slightly different. To show the validity of the approach, we implemented the propagation delay estimator on top of the FLOPSYNC-2 synchronization scheme. Experimental results prove the feasibility of measuring propagation delays using off-the-shelf microcontrollers and radio transceivers, and show how the proposed solution allows to achieve sub-microsecond clock synchronization even for networks where propagation delays are significant.
Neuromorphic Architecture for the Hierarchical Temporal Memory
Venue: arXiv cs.AI 2018
Year: 2018

A biomimetic machine intelligence algorithm, that holds promise in creating invariant representations of spatiotemporal input streams is the hierarchical temporal memory (HTM). This unsupervised online algorithm has been demonstrated on several machine-learning tasks, including anomaly detection. Significant effort has been made in formalizing and applying the HTM algorithm to different classes of problems. There are few early explorations of the HTM hardware architecture, especially for the earlier version of the spatial pooler of HTM algorithm. In this article, we present a full-scale HTM architecture for both spatial pooler and temporal memory. Synthetic synapse design is proposed to address the potential and dynamic interconnections occurring during learning. The architecture is interweaved with parallel cells and columns that enable high processing speed for the HTM. The proposed architecture is verified for two different datasets: MNIST and the European number plate font (EUNF), with and without the presence of noise. The spatial pooler architecture is synthesized on Xilinx ZYNQ-7, with 91.16% classification accuracy for MNIST and 90\% accuracy for EUNF, with noise. For the temporal memory sequence prediction, first and second order predictions are observed for a 5-number long sequence generated from EUNF dataset and 95% accuracy is obtained. Moreover, the proposed hardware architecture offers 1364X speedup over the software realization. These results indicate that the proposed architecture can serve as a digital core to build the HTM in hardware and eventually as a standalone self-learning system.
The Case for API Communicability Evaluation: Introducing API-SI with Examples from Keras
Venue: arXiv cs.SE 2018
Year: 2018

In addition to their vital role in professional software development, Application Programming Interfaces (APIs) are now increasingly used by non-professional programmers, including end users, scientists and experts from other domains. Therefore, good APIs must meet old and new user requirements. Most of the re-search on API evaluation and design derives from user-centered, cognitive perspectives on human-computer interaction. As an alternative, we present a lower-threshold variant of a previously proposed semiotic API evaluation tool. We illustrate the procedures and power of this variant, called API Signification Inspection (API-SI), with Keras, a Deep Learning API. The illustration also shows how the method can complement and fertilize API usability studies. Additionally, API-SI is packaged as an introductory semiotic tool that API designers and researchers can use to evaluate the communication of design intent and product rationale to other programmers through implicit and explicit signs thereof, encountered in the API structure, behavior and documentation.
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fitting
Memristor - The fictional circuit element
Venue: arXiv cs.ET 2018
Year: 2018

The memory resistor abbreviated memristor was a harmless postulate in 1971. In the decade since 2008, a device claiming to be the missing memristor is on the prowl, seeking recognition as a fundamental circuit element, sometimes wanting electronics textbooks to be rewritten, always promising remarkable digital, analog and neuromorphic computing possibilities. A systematic discussion about the fundamental nature of the device is almost universally absent. This report investigates the assertion that the memristor is a fundamental passive circuit element, from the perspective that electrical engineering is the science of charge management. With a periodic table of fundamental elements, we demonstrate that there can only be three fundamental passive circuit elements. The ideal memristor is shown to be an unphysical active device. A vacancy transport model further reveals that a physically realizable memristor is a nonlinear composition of two resistors with active hysteresis.
Creation and Fixing of Lithography Hotspots with Synopsys Tools
Venue: arXiv cs.OH 2018
Year: 2018

At advanced process nodes, pattern matching techniques have been used in the detection of lithography hotspots, which can affect yields of manufactured integrated circuits. Although commercial pattern matching and in-design hotspot fixing tools have been developed, engineers still need to verify that specific hotspot patterns in routed designs can indeed be detected or even repaired by software tools. Therefore, there is the need to create test cases with which targeted hotspot patterns can be generated in routed layouts by using an APR (automatic placement and routing) tool. In this paper, we propose a methodology of creating hotspot patterns in the routing space by using Synopsys tools. Also, methods for repairing hotspots during the physical design phase are presented. With the use of the proposed hotspot creation methodology, we can generate routed designs containing targeted hotspot patterns. As a result, the effectiveness of hotspot detection rules, hotspot fixing guidance rules, and relevant software tool functions can be verified.
Context-Aware DFM Rule Analysis and Scoring Using Machine Learning
Venue: arXiv cs.OH 2018
Year: 2018

To evaluate the quality of physical layout designs in terms of manufacturability, DFM rule scoring techniques have been widely used in physical design and physical verification phases. However, one major drawback of conventional DFM rule scoring methodologies is that resultant DFM rule scores may not accurate since the scores may not highly correspond to lithography simulation results. For instance, conventional DFM rule scoring methodologies usually use rule-based techniques to compute scores without considering neighboring geometric scenarios of targeted layout shapes. That can lead to inaccurate scoring results since computed DFM rule scores can be either too optimistic or too pessimistic. Therefore, in this paper, we propose a novel approach with the use of machine learning technology to analyze the context of targeted layouts and predict their lithography impacts on manufacturability.
Learning-based Automatic Parameter Tuning for Big Data Analytics Frameworks
Venue: arXiv cs.SE 2018
Year: 2018

Big data analytics frameworks (BDAFs) have been widely used for data processing applications. These frameworks provide a large number of configuration parameters to users, which leads to a tuning issue that overwhelms users. To address this issue, many automatic tuning approaches have been proposed. However, it remains a critical challenge to generate enough samples in a high-dimensional parameter space within a time constraint. In this paper, we present AutoTune--an automatic parameter tuning system that aims to optimize application execution time on BDAFs. AutoTune first constructs a smaller-scale testbed from the production system so that it can generate more samples, and thus train a better prediction model, under a given time constraint. Furthermore, the AutoTune algorithm produces a set of samples that can provide a wide coverage over the high-dimensional parameter space, and searches for more promising configurations using the trained prediction model. AutoTune is implemented and evaluated using the Spark framework and HiBench benchmark deployed on a public cloud. Extensive experimental results illustrate that AutoTune improves on default configurations by 63.70% on average, and on the five state-of-the-art tuning algorithms by 6%-23%.
Ultra Reliable, Low Latency Vehicle-to-Infrastructure Wireless Communications with Edge Computing
Venue: arXiv cs.IT 2018
Year: 2018

Ultra reliable, low latency vehicle-to-infrastructure (V2I) communications is a key requirement for seamless operation of autonomous vehicles (AVs) in future smart cities. To this end, cellular small base stations (SBSs) with edge computing capabilities can reduce the end-to-end (E2E) service delay by processing requested tasks from AVs locally, without forwarding the tasks to a remote cloud server. Nonetheless, due to the limited computational capabilities of the SBSs, coupled with the scarcity of the wireless bandwidth resources, minimizing the E2E latency for AVs and achieving a reliable V2I network is challenging. In this paper, a novel algorithm is proposed to jointly optimize AVs-to-SBSs association and bandwidth allocation to maximize the reliability of the V2I network. By using tools from labor matching markets, the proposed framework can effectively perform distributed association of AVs to SBSs, while accounting for the latency needs of AVs as well as the limited computational and bandwidth resources of SBSs. Moreover, the convergence of the proposed algorithm to a core allocation between AVs and SBSs is proved and its ability to capture interdependent computational and transmission latencies for AVs in a V2I network is characterized. Simulation results show that by optimizing the E2E latency, the proposed algorithm substantially outperforms conventional cell association schemes, in terms of service reliability and latency.
Doubly F-Bounded Generics
Venue: arXiv cs.PL 2018
Year: 2018

In this paper we suggest how f-bounded generics in nominally-typed OOP can be extended to the more general notion we call `doubly f-bounded generics' and we suggest how doubly f-bounded generics can be reasoned about. We also (attempt to) prove, using a coinductive argument, that our reasoning method is mathematically sound.
EEG-based Evaluation of Cognitive Workload Induced by Acoustic Parameters for Data Sonification
Venue: arXiv cs.HC 2018
Year: 2018

Data Visualization has been receiving growing attention recently, with ubiquitous smart devices designed to render information in a variety of ways. However, while evaluations of visual tools for their interpretability and intuitiveness have been commonplace, not much research has been devoted to other forms of data rendering, eg, sonification. This work is the first to automatically estimate the cognitive load induced by different acoustic parameters considered for sonification in prior studies. We examine cognitive load via (a) perceptual data-sound mapping accuracies of users for the different acoustic parameters, (b) cognitive workload impressions explicitly reported by users, and (c) their implicit EEG responses compiled during the mapping task. Our main findings are that (i) low cognitive load-inducing (ie, more intuitive) acoustic parameters correspond to higher mapping accuracies, (ii) EEG spectral power analysis reveals higher $α$ band power for low cognitive load parameters, implying a congruent relationship between explicit and implicit user responses, and (iii) Cognitive load classification with EEG features achieves a peak F1-score of 0.64, confirming that reliable workload estimation is achievable with user EEG data compiled using wearable sensors.
Effect of secular trend in drug effectiveness study in real world data
Venue: arXiv cs.LG 2018
Year: 2018

We discovered secular trend bias in a drug effectiveness study for a recently approved drug. We compared treatment outcomes between patients who received the newly approved drug and patients exposed to the standard treatment. All patients diagnosed after the new drug's approval date were considered. We built a machine learning causal inference model to determine patient subpopulations likely to respond better to the newly approved drug. After identifying the presence of secular trend bias in our data, we attempted to adjust for the bias in two different ways. First, we matched patients on the number of days from the new drug's approval date that the patient's treatment (new or standard) began. Second, we included a covariate in the model for the number of days between the date of approval of the new drug and the treatment (new or standard) start date. Neither approach completely mitigated the bias. Residual bias we attribute to differences in patient disease severity or other unmeasured patient characteristics. Had we not identified the secular trend bias in our data, the causal inference model would have been interpreted without consideration for this underlying bias. Being aware of, testing for, and handling potential bias in the data is essential to diminish the uncertainty in AI modeling.
The rent's too high: Self-archive for fair online publication costs
Venue: arXiv cs.DL 2018
Year: 2018

The main contributors of scientific knowledge, researchers, generally aim to disseminate their findings far and wide. And yet, publishing companies have largely kept these findings behind a paywall. With digital publication technology markedly reducing cost, this enduring wall seems disproportionate and unjustified; moreover, it has sparked a topical exchange concerning how to modernize academic publishing. This discussion, however, seems to focus on how to compensate major publishers for providing open access through a "pay to publish" model, in turn transferring financial burdens from libraries to authors and their funders. Large publishing companies, including Elsevier, Springer Nature, Wiley, PLoS, and Frontiers, continue to earn exorbitant revenues each year, hundreds of millions of dollars of which now come from processing charges for open-access articles. A less expensive and equally accessible alternative exists: widespread self-archiving of peer-reviewed articles. All we need is awareness of this alternative and the will to employ it