OpenRaiser/Intern-Atlas huggingface dataset:
A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems
Venue: arXiv cs.NE 2019
Year: 2019

In electroencephalogram (EEG) signal processing, finding the appropriate information from a dataset has been a big challenge for successful signal classification. The feature selection methods make it possible to solve this problem; however, the method selection is still under investigation to find out which feature can perform the best to extract the most proper features of the signal to improve the classification performance. In this study, we use the genetic algorithm (GA), a heuristic searching algorithm, to find the optimum combination of the feature extraction methods and the classifiers, in the brain-computer interface (BCI) applications. A BCI system can be practical if and only if it performs with high accuracy and high speed alongside each other. In the proposed method, GA performs as a searching engine to find the best combination of the features and classifications. The features used here are Katz, Higuchi, Petrosian, Sevcik, and box-counting dimension (BCD) feature extraction methods. These features are applied to the wavelet subbands and are classified with four classifiers such as adaptive neuro-fuzzy inference system (ANFIS), fuzzy k-nearest neighbors (FKNN), support vector machine (SVM) and linear discriminant analysis (LDA). Due to the huge number of features, the GA optimization is used to find the features with the optimum fitness value (FV). Results reveal that Katz fractal feature estimation method with LDA classification has the best FV. Consequently, due to the low computation time of the first Daubechies wavelet transformation in comparison to the original signal, the final selected methods contain the fractal features of the first coefficient of the detail subbands.
DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data
Venue: arXiv cs.NE 2019
Year: 2019

Much sequential data exhibits highly non-uniform information distribution. This cannot be correctly modeled by traditional Long Short-Term Memory (LSTM). To address that, recent works have extended LSTM by adding more activations between adjacent inputs. However, the approaches often use a fixed depth, which is at the step of the most information content. This one-size-fits-all worst-case approach is not satisfactory, because when little information is distributed to some steps, shallow structures can achieve faster convergence and consume less computation resource. In this paper, we develop a Depth-Adaptive Long Short-Term Memory (DA-LSTM) architecture, which can dynamically adjust the structure depending on information distribution without prior knowledge. Experimental results on real-world datasets show that DA-LSTM costs much less computation resource and substantially reduce convergence time by $41.78\%$ and $46.01 \%$, compared with Stacked LSTM and Deep Transition LSTM, respectively.
Exploring Mixed Integer Programming Reformulations for Virtual Machine Placement with Disk Anti-Colocation Constraints
Venue: arXiv cs.DC 2019
Year: 2019

One of the important problems for datacenter resource management is to place virtual machines (VMs) to physical machines (PMs) such that certain cost, profit or performance objective is optimized, subject to various constraints. In this paper, we consider an interesting and difficult VM placement problem with disk anti-colocation constraints: a VM's virtual disks should be spread out across the physical disks of its assigned PM. For solutions, we use the mixed integer programming (MIP) formulations and algorithms. However, a challenge is the potentially long computation time of the MIP algorithms. In this paper, we explore how reformulation of the problem can help to reduce the computation time. We develop two reformulations, by redefining the variables, for our VM placement problem and evaluate the computation time of all three formulations. We show that they have vastly different computation time. All three formulations can be useful, but for different problem instances. They all should be kept in the toolbox for tackling the problem. Out of the three, formulation COMB is especially flexible and versatile, and it can solve large problem instances.
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
Venue: arXiv cs.AI 2019
Year: 2019

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018; Bos, Azoulay, & Martin-Loeches, 2019; Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication.
Distributed Cooperative Caching for VoD with Geographic Constraints
Venue: arXiv cs.NI 2019
Year: 2019

We consider caching of video streams in a cellular network in which each base station is equipped with a cache. Video streams are partitioned into multiple substreams and the goal is to place substreams in caches such that the residual backhaul load is minimized. We consider two coding mechanisms for the substreams: Layered coding (LC) mechanism and Multiple Description Coding (MDC). We develop a distributed asynchronous algorithm for deciding which files to store in which cache to minimize the residual bandwidth, i.e., the cost for downloading the missing substreams of the user's requested video with a certain video quality from the gateway (i.e., the main server). We show that our algorithm converges rapidly. Also, we show that MDC partitioning is better than LC mechanism when the most popular content is stored in caches; however, our algorithm diminishes this difference.
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Venue: arXiv cs.AI 2019
Year: 2019

Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an interactive explanation to propose an interaction protocol. We follow a bottom-up approach to derive the model by analysing transcripts of different explanation dialogue types with 398 explanation dialogues. We use grounded theory to code and identify key components of an explanation dialogue. We formalize the model using the agent dialogue framework (ADF) as a new dialogue type and then evaluate it in a human-agent interaction study with 101 dialogues from 14 participants. Our results show that the proposed model can closely follow the explanation dialogues of human-agent conversations.
An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives
Venue: arXiv cs.SE 2019
Year: 2019

Recent advances in natural-language processing and data analysis allow software bots to become virtual team members, providing an additional set of automated eyes and additional perspectives for informing and supporting teamwork. In this paper, we propose employing chatbots in the domain of software development with a focus on supporting analyses and measurements of teams' project data. The software project artifacts produced by agile teams during regular development activities, e.g. commits in a version control system, represent detailed information on how a team works and collaborates. Analyses of this data are especially relevant for agile retrospective meetings, where adaptations and improvements to the executed development process are discussed. Development teams can use these measurements to track the progress of identified improvement actions over development iterations. Chatbots provide a convenient user interface for interacting with the outcomes of retrospectives and the associated measurements in a chat-based channel that is already being employed by team members.
A Survey of Network-based Intrusion Detection Data Sets
Venue: arXiv cs.CR 2019
Year: 2019

Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based network data in detail. The paper identifies 15 different properties to assess the suitability of individual data sets for specific evaluation scenarios. These properties cover a wide range of criteria and are grouped into five categories such as data volume or recording environment for offering a structured search. Based on these properties, a comprehensive overview of existing data sets is given. This overview also highlights the peculiarities of each data set. Furthermore, this work briefly touches upon other sources for network-based data such as traffic generators and traffic repositories. Finally, we discuss our observations and provide some recommendations for the use and creation of network-based data sets.
A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems
Venue: arXiv cs.CR 2019
Year: 2019

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design.
Towards a Uniform Architecture for the Efficient Implementation of 2D and 3D Deconvolutional Neural Networks on FPGAs
Venue: arXiv cs.DC 2019
Year: 2019

Three-dimensional deconvolution is widely used in many computer vision applications. However, most previous works have only focused on accelerating 2D deconvolutional neural networks (DCNNs) on FPGAs, while the acceleration of 3D DCNNs has not been studied in depth as they have higher computational complexity and sparsity than 2D DCNNs. In this paper, we focus on the acceleration of both 2D and 3D DCNNs on FPGAs by proposing efficient schemes for mapping 2D and 3D DCNNs on a uniform architecture. By implementing our design on the Xilinx VC709 platform for four real-life 2D and 3D DCNNs, we can achieve up to 3.0 TOPS with high hardware efficiency. Comparisons with CPU and GPU solutions demonstrate that we can achieve an improvement of up to 63.3X in throughput relative to a CPU solution and an improvement of up to 8.3X in energy efficiency compared to a GPU solution.
HRSON: Home-based Routing for Smartphones in Opportunistic Networks
Venue: arXiv cs.NI 2019
Year: 2019

Opportunistic networks are delay-tolerant networks which enable network connectivity while there is limited access to network infrastructure, such as natural disaster happenings. Since opportunistic networks use store-carry-forward mechanism to deliver data, routing algorithms have significant impact on successful data delivery. Due to the Operating System restrictions, creating an opportunistic network using ad-hoc model is not feasible on smartphones and to the best of our knowledge, none of common zero-knowledge routing algorithms have been proposed for a non-ad hoc communication model. In this paper, we propose HRSON, a zero-knowledge routing algorithm in a self-organizing approach that an opportunistic infrastructure-based WiFi network is built to use smartphones. We have deployed our approach in simulated scenario of working days of people, whom are using smartphones, on Helsinki map comparing to common zero-knowledge routing algorithms. The results show that our solution increases the average delivery-rate and lowers delay and commutation overhead.
Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Venue: arXiv cs.CV 2019
Year: 2019

We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single human model. For each motion sequence per-frame ground truth geometry and ground truth skeleton are given. The dataset also contains skinning weights of the human model. More information about the dataset can be found at: https://research.csiro.au/robotics/our-work/databases/synthetic-human-model-dataset/
Novel quantitative indicators of digital ophthalmoscopy image quality
Venue: arXiv cs.CV 2019
Year: 2019

With the advent of smartphone indirect ophthalmoscopy, teleophthalmology - the use of specialist ophthalmology assets at a distance from the patient - has experienced a breakthrough, promising enormous benefits especially for healthcare in distant, inaccessible or opthalmologically underserved areas, where specialists are either unavailable or too few in number. However, accurate teleophthalmology requires high-quality ophthalmoscopic imagery. This paper considers three feature families - statistical metrics, gradient-based metrics and wavelet transform coefficient derived indicators - as possible metrics to identify unsharp or blurry images. By using standard machine learning techniques, the suitability of these features for image quality assessment is confirmed, albeit on a rather small data set. With the increased availability and decreasing cost of digital ophthalmoscopy on one hand and the increased prevalence of diabetic retinopathy worldwide on the other, creating tools that can determine whether an image is likely to be diagnostically suitable can play a significant role in accelerating and streamlining the teleophthalmology process. This paper highlights the need for more research in this area, including the compilation of a diverse database of ophthalmoscopic imagery, annotated with quality markers, to train the Point of Acquisition error detection algorithms of the future.
Twitter Speaks: A Case of National Disaster Situational Awareness
Venue: arXiv cs.SI 2019
Year: 2019

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.
Allocation of Computation-Intensive Graph Jobs over Vehicular Clouds in IoV
Venue: arXiv cs.NI 2019
Year: 2019

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy on-board workloads and enable on-demand provisioning of computational resources. In this paper, we present a novel framework for vehicular clouds that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over vehicular clouds is formulated as a non-linear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize job completion time and data exchange cost. The problem is addressed for two scenarios: low-traffic and rush-hours. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree based subgraph isomorphism. We evaluate the performance of our proposed algorithms through extensive simulations.
An Inverting-Tube Clutching Contractile Soft Pneumatic Actuator
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents the simple synergistic combination of a novel contracting soft pneumatic actuator with a soft clutch (linear brake). The device is designated the Inverting-tube Vacuum ACtuator with Clutch (InVACC). The actuator alone (no clutch) is designated "InVAC" and uses vacuum pressure to invert a thin tube into a shorter section of reinforced flexible tubing. The inverting tube acts as rolling diaphragm and a flexible tendon. This allows the actuator to contract to one third of its extended length. The contractile-force-per-unit-pressure is approximately constant over the stroke. The theoretical maximum of this force is the product of the vacuum gauge pressure and half the interior cross-sectional area of the tube. The experimental evaluation revealed hysteretic losses that depend on the actuation direction and rate. With -81 kPa, the prototype produced 12.7 N of tension during extension and 7.5 N during retraction. The reinforced tubing of the InVAC was integrated with an inner collapsible "clutching" tube to create an InVACC. The clutch is engaged by applying a positive pressure between the reinforced tube and the clutching tube, which collapses the clutching tube onto the flexible tendon. With a pressure of 50 kPa, the InVACC clutch tested in this work was able to support a peak tensile load of 120 N before slipping. Though the fatigue life of the current prototypes is limited, improved fabrication methods for this novel actuator/clutch concept will enable new applications in robotics and wearable haptic systems.
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Venue: arXiv cs.CV 2019
Year: 2019

Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring. Without human-made assumptions and numerical algorithms, they are able to restore images with fewer artifacts and better perceptual quality. However, in practice, we also find some of their drawbacks. Without the theoretical guidance, these methods can not perform well when the motion is complex and sometimes generate unreasonable results. In this paper, for overcoming these drawbacks, we integrate deep convolution neural networks into conventional deblurring framework. Specifically, we build Stacked Estimation Residual Net (SEN) to estimate the motion flow map and Recurrent Prior Generative and Adversarial Net (RP-GAN) to learn the implicit image prior in the optimization model. Comparing with state-of-the-art end-to-end learning-based methods, our method restores reasonable details and shows better generalization ability.
The Privacy Blanket of the Shuffle Model
Venue: arXiv cs.LG 2019
Year: 2019

This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can track back an input to a specific user, in the shuffle model users submit their privatized inputs to a server anonymously. This setup yields a trust model which sits in between the classical curator and local models for differential privacy. The shuffle model is the core idea in the Encode, Shuffle, Analyze (ESA) model introduced by Bittau et al. (SOPS 2017). Recent work by Cheu et al. (EUROCRYPT 2019) analyzes the differential privacy properties of the shuffle model and shows that in some cases shuffled protocols provide strictly better accuracy than local protocols. Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user. In this context, we make three contributions. First, we provide an optimal single message protocol for summation of real numbers in the shuffle model. Our protocol is very simple and has better accuracy and communication than the protocols for this same problem proposed by Cheu et al. Optimality of this protocol follows from our second contribution, a new lower bound for the accuracy of private protocols for summation of real numbers in the shuffle model. The third contribution is a new amplification bound for analyzing the privacy of protocols in the shuffle model in terms of the privacy provided by the corresponding local randomizer. Our amplification bound generalizes the results by Erlingsson et al. to a wider range of parameters, and provides a whole family of methods to analyze privacy amplification in the shuffle model.