OpenRaiser/Intern-Atlas huggingface dataset:
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Venue: arXiv cs.CV 2019
Year: 2019

Researches have shown that diet recording can help people increase awareness of food intake and improve nutrition management, and thereby maintain a healthier life. Recently, researchers have been working on smartphone-based diet recording methods and applications that help users accomplish two tasks: record what they eat and how much they eat. Although the former task has made great progress through adopting image recognition technology, it is still a challenge to estimate the volume of foods accurately and conveniently. In this paper, we propose a novel method, named MUSEFood, for food volume estimation. MUSEFood uses the camera to capture photos of the food, but unlike existing volume measurement methods, MUSEFood requires neither training images with volume information nor placing a reference object of known size while taking photos. In addition, considering the impact of different containers on the contour shape of foods, MUSEFood uses a multi-task learning framework to improve the accuracy of food segmentation, and uses a differential model applicable for various containers to further reduce the negative impact of container differences on volume estimation accuracy. Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size. The experiments on real foods indicate that MUSEFood outperforms state-of-the-art approaches, and highly improves the speed of food volume estimation.
An Adversarial Risk Analysis Framework for Cybersecurity
Venue: arXiv cs.CR 2019
Year: 2019

Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially affecting them, prioritise the defence of their assets and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both adversarial and non-intentional threats and the use of insurance as part of the security portfolio. A case study illustrating the proposed framework is presented, serving as template for more complex cases.
The epidemiology of lateral movement: exposures and countermeasures with network contagion models
Venue: arXiv cs.CR 2019
Year: 2019

An approach is developed for analyzing computer networks to identify systems and accounts that are at particular risk of compromise by an adversary seeking to move laterally through the network via authentication. The dynamics of the adversary are modeled as a contagion spreading across systems linked via authentication relationships derived from Administrator account access and active session data. The adversary is assumed to traverse the network via credential chaining, where the adversary steals credentials from one system, uses them to authenticate to another, and repeats the process. Graph topology measures are used to analyze different contagion models applied to a real Windows network for three primary exposures by identifying: accounts which, either individually or collectively, provide wide and far-reaching access to many systems across the network; accounts with notable privilege escalation liability; and "gatekeeper" systems through which the adversary must pass in order to reach critical assets. The approach can be used to test how different mitigations and countermeasures affect these exposures; for example, we find that disabling remote logins by local accounts and implementing protections that prevent the caching of credentials on remote hosts can substantially curtail lateral movement and privilege escalation.
A Note on KL-UCB+ Policy for the Stochastic Bandit
Venue: arXiv cs.LG 2019
Year: 2019

A classic setting of the stochastic K-armed bandit problem is considered in this note. In this problem it has been known that KL-UCB policy achieves the asymptotically optimal regret bound and KL-UCB+ policy empirically performs better than the KL-UCB policy although the regret bound for the original form of the KL-UCB+ policy has been unknown. This note demonstrates that a simple proof of the asymptotic optimality of the KL-UCB+ policy can be given by the same technique as those used for analyses of other known policies.
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
Venue: arXiv cs.CV 2019
Year: 2019

Neuromorphic image sensors produce activity-driven spiking output at every pixel. These low-power consuming imagers which encode visual change information in the form of spikes help reduce computational overhead and realize complex real-time systems; object recognition and pose-estimation to name a few. However, there exists a lack of algorithms in event-based vision aimed towards capturing invariance to transformations. In this work, we propose a methodology for recognizing objects invariant to their pose with the Dynamic Vision Sensor (DVS). A novel slow-ELM architecture is proposed which combines the effectiveness of Extreme Learning Machines and Slow Feature Analysis. The system, tested on an Intel Core i5-4590 CPU, can perform 10,000 classifications per second and achieves 1% classification error for 8 objects with views accumulated over 90 degrees of 2D pose.
Resource Allocation for Loss Tolerant Video Streaming in eMBMS
Venue: arXiv cs.NI 2019
Year: 2019

Bandwidth hungry video content has become the dominant contributor to the data traffic world over. Cellular networks are constantly evolving to meet the growing traffic demands. Over the past few years, wireless multicast has been garnering a lot of attention as a means of efficient resource utilization. Multicast transmission lets spectral resources to be shared between users streaming the same content. Even though multicast transmission allows to serve multiple users on the same resources, in order to serve all these users successfully, the base station cannot transmit the content at a rate greater than that decodable by the user with the worst channel conditions. In this paper, we propose a way to overcome this bottleneck. Video streaming services can sustain a certain amount of packet loss without any significant degradation in the quality experienced by the users. We leverage this loss tolerant nature of video streaming applications to improve the performance of multicast video services in LTE and 5G. We convert the problem of resource allocation for loss tolerant multicasting into the problem of stabilizing a queueing system. We then propose two throughput optimal Maximum Weight (MW) policies that successfully stabilize the constructed queueing system. However, brute force implementation of MW policies is mostly NP-hard. To overcome this, we propose a maximum weight bipartite matching approach that results in a polynomial time implementation of the proposed policies. We also evaluate the performance of our policies via extensive simulations.
CVIT-MT Systems for WAT-2018
Venue: arXiv cs.CL 2019
Year: 2019

This document describes the machine translation system used in the submissions of IIIT-Hyderabad CVIT-MT for the WAT-2018 English-Hindi translation task. Performance is evaluated on the associated corpus provided by the organizers. We experimented with convolutional sequence to sequence architectures. We also train with additional data obtained through backtranslation.
A Matrix-in-matrix Neural Network for Image Super Resolution
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers. However, their application is quite limited since they require high computing power. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR net work named matrixed channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Conclusions can be drawn from our extensive benchmark experiments that the proposed models achieve better performance with much fewer multiply-adds and parameters. Our models will be made publicly available.
A New Lower Bound for Semigroup Orthogonal Range Searching
Venue: arXiv cs.DS 2019
Year: 2019

We report the first improvement in the space-time trade-off of lower bounds for the orthogonal range searching problem in the semigroup model, since Chazelle's result from 1990. This is one of the very fundamental problems in range searching with a long history. Previously, Andrew Yao's influential result had shown that the problem is already non-trivial in one dimension~\cite{Yao-1Dlb}: using $m$ units of space, the query time $Q(n)$ must be $Ω( α(m,n) + \frac{n}{m-n+1})$ where $α(\cdot,\cdot)$ is the inverse Ackermann's function, a very slowly growing function. In $d$ dimensions, Bernard Chazelle~\cite{Chazelle.LB.II} proved that the query time must be $Q(n) = Ω( (\log_βn)^{d-1})$ where $β= 2m/n$. Chazelle's lower bound is known to be tight for when space consumption is `high' i.e., $m = Ω(n \log^{d+\varepsilon}n)$. We have two main results. The first is a lower bound that shows Chazelle's lower bound was not tight for `low space': we prove that we must have $m (n) = Ω(n (\log n \log\log n)^{d-1})$. Our lower bound does not close the gap to the existing data structures, however, our second result is that our analysis is tight. Thus, we believe the gap is in fact natural since lower bounds are proven for idempotent semigroups while the data structures are built for general semigroups and thus they cannot assume (and use) the properties of an idempotent semigroup. As a result, we believe to close the gap one must study lower bounds for non-idempotent semigroups or building data structures for idempotent semigroups. We develope significantly new ideas for both of our results that could be useful in pursuing either of these directions.
Efficient Smoothing of Dilated Convolutions for Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into convolutional filters, they enable the use of larger receptive fields without increasing the original kernel size. Even though this allows for the inexpensive capturing of features at different scales, the structure of the dilated convolutional filter leads to a loss of information. We hypothesise that inexpensive modifications to Dilated Convolutional Neural Networks, such as additional averaging layers, could overcome this limitation. In this project we test this hypothesis by evaluating the effect of these modifications for a state-of-the art image segmentation system and compare them to existing approaches with the same objective. Our experiments show that our proposed methods improve the performance of dilated convolutions for image segmentation. Crucially, our modifications achieve these results at a much lower computational cost than previous smoothing approaches.
ReviewerNet: Visualizing Citation and Authorship Relations for Finding Reviewers
Venue: arXiv cs.DL 2019
Year: 2019

We propose ReviewerNet, an online, interactive visualization system aimed to improve the reviewer selection process in the academic domain. Given a paper submitted for publication, we assume that good candidate reviewers can be chosen among the authors of a small set of relevant and pertinent papers; ReviewerNet supports the construction of such set of papers, by visualizing and exploring a literature citation network. Then, the system helps to select reviewers that are both well distributed in the scientific community and that do not have any conflict-of-interest, by visualising the careers and co-authorship relations of candidate reviewers. The system is publicly available, and it has been evaluated by a set of experienced researchers in the field of Computer Graphics.
Ensemble Clustering for Graphs: Comparisons and Applications
Venue: arXiv cs.LG 2019
Year: 2019

We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. We validated our approach by replicating a study comparing graph clustering algorithms over benchmark graphs, showing that ECG outperforms the leading algorithms. In this paper, we extend our comparison by considering a wider range of parameters for the benchmark, generating graphs with different properties. We provide new experimental results showing that the ECG algorithm alleviates the well-known resolution limit issue, and that it leads to better stability of the partitions. We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph, and to zoom in on the sub-graph most closely associated with seed vertices. Finally, we illustrate further applications of ECG by comparing it to previous results for community detection on weighted graphs, and community-aware anomaly detection.
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication
Venue: arXiv cs.NI 2019
Year: 2019

Future in-vehicular networks will be based on Ethernet. The IEEE Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time requirements in future car communication. Software-Defined Networking (SDN) extends the Ethernet control plane with a programming option that can add much value to the resilience, security, and adaptivity of the automotive environment. In this work, we derive a first concept for combining Software-Defined Networking with Time-Sensitive Networking along with an initial evaluation. Our measurements are performed via a simulation that investigates whether an SDN architecture is suitable for time-critical applications in the car. Our findings indicate that the control overhead of SDN can be added without a delay penalty for the TSN traffic when protocols are mapped properly.
Event-triggered Pulse Control with Model Learning (if Necessary)
Venue: arXiv cs.SY 2019
Year: 2019

In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
Venue: arXiv cs.LG 2019
Year: 2019

We study the problem of online learning with non-convex losses, where the learner has access to an offline optimization oracle. We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T^{-1/2})$ in this setting. This improves upon the previous best-known regret rate of $O(T^{-1/3})$ for FTPL. We further show that an optimistic variant of FTPL achieves better regret bounds when the sequence of losses encountered by the learner is `predictable'.
Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings
Venue: arXiv cs.CL 2019
Year: 2019

The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that metadata have for discovering scientific data, it is crucial that they be represented in a uniform way that can be queried effectively. One step toward uniformly-represented metadata is to normalize the multiple, distinct field names used in metadata (e.g., lat lon, lat and long) to describe the same type of value. To that end, we present a new method based on clustering and embeddings (i.e., vector representations of words) to align metadata field names with ontology terms. We apply our method to biomedical metadata by generating embeddings for terms in biomedical ontologies from the BioPortal repository. We carried out a comparative study between our method and the NCBO Annotator, which revealed that our method yields more and substantially better alignments between metadata and ontology terms.
Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.
Elaborating Inductive Definitions and Course-of-Values Induction in Cedille
Venue: arXiv cs.PL 2019
Year: 2019

In the Calculus of Dependent Lambda Eliminations (CDLE), a pure Curry-style type theory, it is possible to generically λ-encode inductive datatypes which support course-of-values (CoV) induction. We present a datatype subsystem for Cedille (an implementation of CDLE) that provides this feature to programmers through convenient notation for declaring datatypes and for defining functions over them by case analysis and fixpoint-style recursion guarded by a type-based termination checker. We demonstrate that this does not require extending CDLE by showing how datatypes and functions over them elaborate to λ-encodings, and proving that this elaboration is type- and value-preserving. This datatype subsystem and elaborator are implemented in Cedille, establishing for the first time a complete translation of inductive definitions to a small pure typed λ-calculus.
A Quantum Annealing-Based Approach to Extreme Clustering
Venue: arXiv cs.LG 2019
Year: 2019

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster.
On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees
Venue: arXiv cs.LG 2019
Year: 2019

Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an optimized distribution for features based on an infinite-dimensional integral operator (depending on input distribution), which is impractical to sample from. Focusing on empirical leverage scores in this paper, we establish an out-of-sample performance bound, revealing an interesting trade-off between the approximated kernel and the eigenvalue decay of another kernel in the domain of random features defined based on data distribution. Our experiments verify that the empirical algorithm consistently outperforms vanilla Monte Carlo sampling, and with a minor modification the method is even competitive to supervised data-dependent kernel learning, without using the output (label) information.
Indexes in Microsoft SQL Server
Venue: arXiv cs.DB 2019
Year: 2019

Indexes are the best apposite choice for quickly retrieving the records. This is nothing but cutting down the number of Disk IO. Instead of scanning the complete table for the results, we can decrease the number of IO's or page fetches using index structures such as B-Trees or Hash Indexes to retrieve the data faster. The most convenient way to consider an index is to think like a dictionary. It has words and its corresponding definitions against those words. The dictionary will have an index on "word" because when we open a dictionary and we want to fetch its corresponding word quickly, then find its definition. The dictionary generally contains just a single index - an index ordered by word. When we modify any record and change the corresponding value of an indexed column in a clustered index, the database might require moving the entire row into a separately new position to maintain the rows in the sorted order. This action is essentially turned into an update query into a DELETE followed by an INSERT, and it decreases the performance of the query. The clustered index in the table can often be available on the primary key or a foreign key column because key values usually do not modify once a record is injected into the database.