OpenRaiser/Intern-Atlas huggingface dataset:
Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports
Venue: arXiv cs.LG 2019
Year: 2019

Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning approaches burdens each development task as time and resource intensive. The vast majority of dense clinical information is stored in written reports, detailing pertinent patient information. The challenge with utilizing natural language data for standard model development is due to the complex nature of the modality. In this research, a model pipeline was developed to utilize an unsupervised approach to train an encoder-language model, a recurrent network, to generate document encodings; which then can be used as features passed into a decoder-classifier model that requires magnitudes less labeled data than previous approaches to differentiate between fine-grained disease classes accurately. The language model was trained on unlabeled radiology reports from the Massachusetts General Hospital Radiology Department (n=218,159) and terminated with a loss of 1.62. The classification models were trained on three labeled datasets of head CT studies of reported patients, presenting large vessel occlusion (n=1403), acute ischemic strokes (n=331), and intracranial hemorrhage (n=4350), to identify a variety of different findings directly from the radiology report data; resulting in AUCs of 0.98, 0.95, and 0.99, respectively, for the large vessel occlusion, acute ischemic stroke, and intracranial hemorrhage datasets. The output encodings are able to be used in conjunction with imaging data, to create models that can process a multitude of different modalities. The ability to automatically extract relevant features from textual data allows for faster model development and integration of textual modality, overall, allowing clinical reports to become a more viable input for more encompassing and accurate deep learning models.
Two Phase Authentication and VPN Based Secured Communication for IoT Home Networks
Venue: arXiv cs.NI 2019
Year: 2019

With the advancement of technology, devices, which are considered non-traditional in terms of internet capabilities, are now being embedded in microprocessors to communicate and these devices are known as IoT devices. This technology has enabled household devices to have the ability to communicate with the internet and a network comprising of such device can create a home IoT network. Such IoT devices are resource constrained and lack high-level security protocols. Thus, security becomes a major issue for such network systems. One way to secure the networks is through reliable authentication protocols and data transfer mechanism. As the household devices are controllable by the users remotely, they are accessed over the internet. Therefore, there should also be a method to make the communication over the internet between IoT devices and the users more secured. This paper proposes a two-phase authentication protocol for authentication purposes and a VPN based secure channel creation for the communication of the devices in the network. Furthermore, the paper discusses the Elliptic Curve Cryptography as a viable alternative to RSA for a more efficient Key exchange mechanism for low-powered IoT devices in the network.
Software defect prediction with zero-inflated Poisson models
Venue: arXiv cs.SE 2019
Year: 2019

In this work we apply several Poisson and zero-inflated models for software defect prediction. We apply different functions from several R packages such as pscl, MASS, R2Jags and the recent glmmTMB. We test the functions using the Equinox dataset. The results show that Zero-inflated models, fitted with either maximum likelihood estimation or with Bayesian approach, are slightly better than other models, using the AIC as selection criterion.
Forgotten @ Scale: A Practical Solution for Implementing the Right To Be Forgotten in Large-Scale Systems
Venue: arXiv cs.CR 2019
Year: 2019

The European General Data Protection Regulation asserts data subjects' right to be forgotten, i.e., their right to request that all their personal data be deleted from an organizations' data stores. However, fulfilling such requests in large-scale systems is technically challenging. It requires that organizations keep track of all locations in which an individual's data is stored, be able to access and delete it in a reasonable time frame, and be able to prove that all such data was in fact deleted. In addition, organizations must cope with complexities such as multiple, distributed, and continuously evolving systems of record, complex data retention policies and deletion approval workflows. We present a first design pattern and practical implementation of the right to be forgotten on a large scale in Big Data and cloud environments.
Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab
Venue: arXiv cs.CV 2019
Year: 2019

The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour. The traffic status was given as multi-channel images (one pixel roughly corresponds to 100x100 meters), where one channel indicated the traffic volume, another one the average speed of vehicles, and a third one their rough heading. As part of our work on the competition, we evaluated many different network architectures, analyzed the statistical properties of the given data in detail, and thought about how to transform the problem to be able to take additional spatio-temporal context-information into account, such as the street network, the positions of traffic lights, or the weather. This document summarizes our efforts that led to our best submission, and gives some insights about which other approaches we evaluated, and why they did not work as well as imagined.
Group Centrality Maximization for Large-scale Graphs
Venue: arXiv cs.DS 2019
Year: 2019

The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures it was shown that the problem of finding the most central group is $\mathcal{NP}$-hard. As a result, approximation algorithms to maximize group centralities were introduced recently. Despite a nearly-linear running time, approximation algorithms for group betweenness and (to a lesser extent) group closeness are rather slow on large networks due to high constant overheads. That is why we introduce GED-Walk centrality, a new submodular group centrality measure inspired by Katz centrality. In contrast to closeness and betweenness, it considers walks of any length rather than shortest paths, with shorter walks having a higher contribution. We define algorithms that (i) efficiently approximate the GED-Walk score of a given group and (ii) efficiently approximate the (proved to be $\mathcal{NP}$-hard) problem of finding a group with highest GED-Walk score. Experiments on several real-world datasets show that scores obtained by GED-Walk improve performance on common graph mining tasks such as collective classification and graph-level classification. An evaluation of empirical running times demonstrates that maximizing GED-Walk (in approximation) is two orders of magnitude faster compared to group betweenness approximation and for group sizes $\leq 100$ one to two orders faster than group closeness approximation. For graphs with tens of millions of edges, approximate GED-Walk maximization typically needs less than one minute. Furthermore, our experiments suggest that the maximization algorithms scale linearly with the size of the input graph and the size of the group.
Path Planning Games
Venue: arXiv cs.MA 2019
Year: 2019

Path planning is a fundamental and extensively explored problem in robotic control. We present a novel economic perspective on path planning. Specifically, we investigate strategic interactions among path planning agents using a game theoretic path planning framework. Our focus is on economic tension between two important objectives: efficiency in the agents' achieving their goals, and safety in navigating towards these. We begin by developing a novel mathematical formulation for path planning that trades off these objectives, when behavior of other agents is fixed. We then use this formulation for approximating Nash equilibria in path planning games, as well as to develop a multi-agent cooperative path planning formulation. Through several case studies, we show that in a path planning game, safety is often significantly compromised compared to a cooperative solution.
Comparative Bandwidth Analysis of Low-Power Wireless IoT-Switches
Venue: arXiv cs.NI 2019
Year: 2019

The article presents the research and comparative analysis of the bandwidth of low-power wireless IoT devices as wireless switches. The following IoT devices were investigated: Raspberry Pi 3 Model B and Raspberry Pi Zero W. The DS18B20 and INA219 sensors investigated and analyzed the dependence of FTP multimedia data transmission speed on wireless Wi-Fi network on the temperature of the switch processor, temperature. The environment and the current and voltage consumed by the switch. Advantages of sensors with GPIO interface over analog meters for this experiment are revealed. Much of the work is devoted to the development of automation of results from GPIO interfaces, which helped eliminate human error and get more accurate metrics. Measurement automation was developed using Python 3.7 programming language. Using the INA219 library we were able to obtain current and voltage indicators from the ina219 board. To get temperature indicators sufficiently built into Python libraries to read temperature files in Raspbian. The article focuses on the synchronicity of measurement results records for more accurate analysis. Therefore, an FTP client was developed that measures the download speed of the file from the FTP server and records the results simultaneously with temperature, current and voltage measurements. To this end, attention is drawn to the multithreading in Python programming language and the transmission of commands using TCP sockets in that language. As a result, the dependence of the measured factors was calculated using the Pearson correlation formula. These measurement factors affect the autonomy and energy consumption, which is very important for IoT devices, and therefore, among the devices tested, recommendations were made regarding their choice when used depending on the conditions.
On a Decentralized $(Δ{+}1)$-Graph Coloring Algorithm
Venue: arXiv cs.DS 2019
Year: 2019

We consider a decentralized graph coloring model where each vertex only knows its own color and whether some neighbor has the same color as it. The networking community has studied this model extensively due to its applications to channel selection, rate adaptation, etc. Here, we analyze variants of a simple algorithm of Bhartia et al. [Proc., ACM MOBIHOC, 2016]. In particular, we introduce a variant which requires only $O(n\logΔ)$ expected recolorings that generalizes the coupon collector problem. Finally, we show that the $O(nΔ)$ bound Bhartia et al. achieve for their algorithm still holds and is tight in adversarial scenarios.
Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Most neural machine translation systems still translate sentences in isolation. To make further progress, a promising line of research additionally considers the surrounding context in order to provide the model potentially missing source-side information, as well as to maintain a coherent output. One difficulty in training such larger-context (i.e. document-level) machine translation systems is that context may be missing from many parallel examples. To circumvent this issue, two-stage approaches, in which sentence-level translations are post-edited in context, have recently been proposed. In this paper, we instead consider the viability of filling in the missing context. In particular, we consider three distinct approaches to generate the missing context: using random contexts, applying a copy heuristic or generating it with a language model. In particular, the copy heuristic significantly helps with lexical coherence, while using completely random contexts hurts performance on many long-distance linguistic phenomena. We also validate the usefulness of tagged back-translation. In addition to improving BLEU scores as expected, using back-translated data helps larger-context machine translation systems to better capture long-range phenomena.
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
Venue: arXiv cs.CL 2019
Year: 2019

Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are abundant, and highly ambiguous, in clinical documents. One of the main obstacles is the lack of large scale, balance labeled data sets. To address the issue, we propose a few-shot learning approach to take advantage of limited labeled data. Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation. Another vital issue is that the existing scarce annotations are noisy and missing. We re-examine and correct an existing dataset for training and collect a test set to evaluate the models fairly especially for rare senses. We train our model on the training set which contains 30 abbreviation terms as categories (on average, 479 samples and 3.24 classes in each term) selected from a public abbreviation disambiguation dataset, and then test on a manually-created balanced dataset (each class in each term has 15 samples). We show that enhancing the sentence representation with topic information improves the performance on small-scale unbalanced training datasets by a large margin, compared to a number of baseline models.
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization
Venue: arXiv cs.LG 2019
Year: 2019

With the growth of adversarial attacks against machine learning models, several concerns have emerged about potential vulnerabilities in designing deep neural network-based intrusion detection systems (IDS). In this paper, we study the resilience of deep learning-based intrusion detection systems against adversarial attacks. We apply the min-max (or saddle-point) approach to train intrusion detection systems against adversarial attack samples in NSW-NB 15 dataset. We have the max approach for generating adversarial samples that achieves maximum loss and attack deep neural networks. On the other side, we utilize the existing min approach [2] [9] as a defense strategy to optimize intrusion detection systems that minimize the loss of the incorporated adversarial samples during the adversarial training. We study and measure the effectiveness of the adversarial attack methods as well as the resistance of the adversarially trained models against such attacks. We find that the adversarial attack methods that were designed in binary domains can be used in continuous domains and exhibit different misclassification levels. We finally show that principal component analysis (PCA) based feature reduction can boost the robustness in intrusion detection system (IDS) using a deep neural network (DNN).
Dividing a Graphical Cake
Venue: arXiv cs.GT 2019
Year: 2019

We consider the classical cake-cutting problem where we wish to fairly divide a heterogeneous resource, often modeled as a cake, among interested agents. Work on the subject typically assumes that the cake is represented by an interval. In this paper, we introduce a generalized setting where the cake can be in the form of the set of edges of an undirected graph. This allows us to model the division of road or cable networks. Unlike in the canonical setting, common fairness criteria such as proportionality cannot always be satisfied in our setting if each agent must receive a connected subgraph. We determine the optimal approximation of proportionality that can be obtained for any number of agents with arbitrary valuations, and exhibit tight guarantees for each graph in the case of two agents. In addition, when more than one connected piece per agent is allowed, we establish the best egalitarian welfare guarantee for each total number of connected pieces. We also study a number of variants and extensions, including when approximate equitability is considered, or when the item to be divided is undesirable (also known as chore division).
Backhaul-aware Uplink Communications in Full-Duplex DBS-aided HetNets
Venue: arXiv cs.NI 2019
Year: 2019

Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical dimension and horizontal dimensions) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP-hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and it is superior to two benchmark algorithms with up to 45.8% throughput improvement.
How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?
Venue: arXiv cs.CL 2019
Year: 2019

Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun's gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While "embedding debiasing" methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words' context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes
Venue: arXiv cs.RO 2019
Year: 2019

Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Among the neural network compression techniques, knowledge distillation is an effective one which forces a simpler student network to mimic the output of a larger teacher network. However, most of such model distillation methods focus on the image-level classification task. Directly adapting these methods to the task of semantic segmentation only brings marginal improvements. In this paper, we propose a simple, yet effective knowledge representation referred to as pixel-wise feature similarities (PFS) to tackle the challenging distillation problem of semantic segmentation. The developed PFS encodes spatial structural information for each pixel location of the high-level convolutional features, which helps guide the distillation process in an easier way. Furthermore, a novel weighted pixel-level soft prediction imitation approach is proposed to enable the student network to selectively mimic the teacher network's output, according to their pixel-wise knowledge-gaps. Extensive experiments are conducted on the challenging datasets of Pascal VOC 2012, ADE20K and Pascal Context. Our approach brings significant performance improvements compared to several strong baselines and achieves new state-of-the-art results.
Computing and Testing Small Connectivity in Near-Linear Time and Queries via Fast Local Cut Algorithms
Venue: arXiv cs.DS 2019
Year: 2019

Consider the following "local" cut-detection problem in a directed graph: We are given a seed vertex $x$ and need to remove at most $k$ edges so that at most $ν$ edges can be reached from $x$ (a "local" cut) or output $\bot$ to indicate that no such cut exists. If we are given query access to the input graph, then this problem can in principle be solved without reading the whole graph and with query complexity depending on $k$ and $ν$. In this paper we consider a slack variant of this problem where, when such a cut exists, we can output a cut with up to $O(kν)$ edges reachable from $x$. We present a simple randomized algorithm spending $O(k^2ν)$ time and $O(kν)$ queries for the above variant, improving in particular a previous time bound of $O(k^{O(k)}ν)$ by Chechik et al. [SODA '17]. We also extend our algorithm to handle an approximate variant. We demonstrate that these local algorithms are versatile primitives for designing substantially improved algorithms for classic graph problems by providing the following three applications. (Throughout, $\tilde O(T)$ hides $\operatorname{polylog}(T)$.) (1) A randomized algorithm for the classic $k$-vertex connectivity problem that takes near-linear time when $k=O(\operatorname{polylog}(n))$, namely $\tilde O(m+nk^3)$ time in undirected graphs. For directed graphs our $\tilde O(mk^2)$-time algorithm is near-linear when $k=O(\operatorname{polylog}(n))$. Our techniques also yield an improved approximation scheme. (2) Property testing algorithms for $k$-edge and -vertex connectivity with query complexities that are near-linear in $k$, exponentially improving the state-of-the-art. This resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [Theor. Comput Sci. '11]. (3) A faster algorithm for computing the maximal $k$-edge connected subgraphs, improving prior work of Chechik et al. [SODA '17].
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We introduce a new definition of surprise and its RL implementation named Variational Assorted Surprise Exploration (VASE). VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent's model and policy. Our experiments show that in continuous control sparse reward environments VASE outperforms other surprise-based exploration techniques.