OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
Venue: arXiv cs.CV 2018
Year: 2018

State-of-the-art person re-identification systems that employ a triplet based deep network suffer from a poor generalization capability. In this paper, we propose a four stream Siamese deep convolutional neural network for person redetection that jointly optimises verification and identification losses over a four image input group. Specifically, the proposed method overcomes the weakness of the typical triplet formulation by using groups of four images featuring two matched (i.e. the same identity) and two mismatched images. This allows us to jointly increase the interclass variations and reduce the intra-class variations in the learned feature space. The proposed approach also optimises over both the identification and verification losses, further minimising intra-class variation and maximising inter-class variation, improving overall performance. Extensive experiments on four challenging datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed approach achieves state-of-the-art performance.
Stochastic Doubly Robust Gradient
Venue: arXiv cs.LG 2018
Year: 2018

When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the missingness depends on some covariates may lead to biased estimation of parameters and even harm the fairness of decision outcome. This paper proposes how to adjust the causal effect of covariates on the missingness when training models using stochastic gradient descent (SGD). Inspired by the design of doubly robust estimator and its theoretical property of double robustness, we introduce stochastic doubly robust gradient (SDRG) consisting of two models: weight-corrected gradients for inverse propensity score weighting and per-covariate control variates for regression adjustment. Also, we identify the connection between double robustness and variance reduction in SGD by demonstrating the SDRG algorithm with a unifying framework for variance reduced SGD. The performance of our approach is empirically tested by showing the convergence in training image classifiers with several examples of missing data.
Study on A High-integrated Cloud-Based Customer Relationship Management System
Venue: arXiv cs.CY 2018
Year: 2018

In the context of the business applications, integrating an on-premise customer Relationship Management (CRM) system with other systems used to be resource-consuming and complicated in terms of data and system interworking. With the help of cloud computing technology, on-cloud business applications have the capability of integrating CRM with ERP and other systems more efficiently. This allows large enterprises as well as small and medium companies (SMEs) manage critical business processes (such as Sales, Marketing, Customer Service, Operations, Finance, Field Service and Project Service Automation) in a unified manner. Therefore, businesses can cover the entire customer lifecycle by effectively utilizing all of these resources. This case study analyses on-demand CRM applications that are part of Microsoft Dynamics 365. By studying the structures and how Dynamics 365 CRM integrates with other applications, this paper provides a reference to companies that plan to switch their on-premise CRM to the Cloud.
Advanced Testing Chain Supporting the Validation of Smart Grid Systems and Technologies
Venue: arXiv cs.SY 2018
Year: 2018

New testing and development procedures and methods are needed to address topics like power system stability, operation and control in the context of grid integration of rapidly developing smart grid technologies. In this context, individual testing of units and components has to be reconsidered and appropriate testing procedures and methods need to be described and implemented. This paper addresses these needs by proposing a holistic and enhanced testing methodology that integrates simulation/software- and hardware-based testing infrastructure. This approach presents the advantage of a testing environment, which is very close to f i eld testing, includes the grid dynamic behavior feedback and is risks-free for the power system, for the equipment under test and for the personnel executing the tests. Furthermore, this paper gives an overview of successful implementation of the proposed testing approach within different testing infrastructure available at the premises of different research institutes in Europe.
Measuring science: irresistible temptations, easy shortcuts and dangerous consequences
Venue: arXiv cs.DL 2018
Year: 2018

In benchmarking international research, although publication and citation analyses should not be used to compare different disciplines, scientometrists frequently fail to resist the temptation to present rankings based on total publications and citations. Such measures are affected by significant distortions, due to the uneven fertility across scientific disciplines and the dishomogeneity of scientific specialisation among nations and universities. In this paper, we provide an indication of the extent of the distortions when comparative bibliometric analyses fail to recognise the range of levels of scientific fertility, not only within a given major disciplinary area but also within the different scientific disciplines encompassed by the same area.
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Background: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, excretion prediction models still have limited accuracy. Aim: This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters. Methods: A pharmacokinetic dataset included 1104 U.S. FDA approved small molecule drugs. The dataset included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state and elimination half-life). The pre-trained model was trained on over 30 million bioactivity data. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. Results: The pharmacokinetic dataset was split into three parts (60:20:20) for training, validation and test by the improved Maximum Dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability, transfer learning and multitask learning improved the model generalization. Conclusions: The integrated transfer learning and multitask learning approach with the improved dataset splitting algorithm was firstly introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.
Reasoning and Facts Explanation in Valuation Based Systems
Venue: arXiv cs.AI 2018
Year: 2018

In the literature, the optimization problem to identify a set of composite hypotheses H, which will yield the $k$ largest $P(H|S_e)$ where a composite hypothesis is an instantiation of all the nodes in the network except the evidence nodes \cite{KSy:93} is of significant interest. This problem is called "finding the $k$ Most Plausible Explanation (MPE) of a given evidence $S_e$ in a Bayesian belief network". The problem of finding $k$ most probable hypotheses is generally NP-hard \cite{Cooper:90}. Therefore in the past various simplifications of the task by restricting $k$ (to 1 or 2), restricting the structure (e.g. to singly connected networks), or shifting the complexity to spatial domain have been investigated. A genetic algorithm is proposed in this paper to overcome some of these restrictions while stepping out from probabilistic domain onto the general Valuation based System (VBS) framework is also proposed by generalizing the genetic algorithm approach to the realm of Dempster-Shafer belief calculus.
Spatially Random Relay Selection for Full/Half-Duplex Cooperative NOMA Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA), where relays are capable of working in either full-duplex (FD) or half-duplex (HD) mode. A number of relays (i.e., $K$ relays) are uniformly distributed within the disc. A pair of RS schemes are considered insightfully: 1) Single-stage RS (SRS) scheme; and 2) Two-stage RS (TRS) scheme. In order to characterize the performance of these two RS schemes, new closed-form expressions for both exact and asymptotic outage probabilities are derived. Based on analytical results, the diversity orders achieved by the pair of RS schemes for FD/HD cooperative NOMA are obtained. Our analytical results reveal that: i) The FD-based RS schemes obtain a zero diversity order, which is due to the influence of loop interference (LI) at the relay; and ii) The HD-based RS schemes are capable of achieving a diversity order of $K$, which is equal to the number of relays. Finally, simulation results demonstrate that: 1) The FD-based RS schemes have better outage performance than HD-based RS schemes in the low signal-to-noise radio (SNR) region; 2) As the number of relays increases, the pair of RS schemes considered are capable of achieving the lower outage probability; and 3) The outage behaviors of FD/HD-based NOMA SRS/TRS schemes are superior to that of random RS (RRS) and orthogonal multiple access (OMA) based RS schemes.
A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
Venue: arXiv cs.CR 2018
Year: 2018

Internet of Things (IoT) is a disruptive technology with applications across diverse domains such as transportation and logistics systems, smart grids, smart homes, connected vehicles, and smart cities. Alongside the growth of these infrastructures, the volume and variety of attacks on these infrastructures has increased highlighting the significance of distinct protection mechanisms. Intrusion detection is one of the distinguished protection mechanisms with notable recent efforts made to establish effective intrusion detection for IoT and IoV. However, unique characteristics of such infrastructures including battery power, bandwidth and processors overheads, and the network dynamics can influence the operation of an intrusion detection system. This paper presents a comprehensive study of existing intrusion detection systems for IoT systems including emerging systems such as Internet of Vehicles (IoV). The paper analyzes existing systems in three aspects: computational overhead, energy consumption and privacy implications. Based on a rigorous analysis of the existing intrusion detection approaches, the paper also identifies open challenges for an effective and collaborative design of intrusion detection system for resource-constrained IoT system in general and its applications such as IoV. These efforts are envisaged to highlight state of the art with respect to intrusion detection for IoT and open challenges requiring specific efforts to achieve efficient intrusion detection within these systems.
Identifying interdisciplinarity through the disciplinary classification of co-authors of scientific publications
Venue: arXiv cs.DL 2018
Year: 2018

The growing complexity of challenges involved in scientific progress demands ever more frequent application of competencies and knowledge from different scientific fields. The present work analyzes the degree of collaboration among scientists from different disciplines in order to identify the most frequent "combinations of knowledge" in research activity. The methodology adopts an innovative bibliometric approach based on the disciplinary affiliation of publication co-authors. The field of observation includes all publications (173,134) indexed in the Science Citation Index Expanded (SCI-E) for the five years 2004-2008, authored by all scientists in the hard sciences (43,223) at Italian universities (68). The analysis examines 205 research fields grouped in nine disciplines. Identifying the fields with the highest potential of interdisciplinary collaboration is useful to inform research polices at national and regional levels, as well as management strategies at the institutional level.
The future of statistical disclosure control
Venue: arXiv cs.CR 2018
Year: 2018

Statistical disclosure control (SDC) was not created in a single seminal paper nor following the invention of a new mathematical technique, rather it developed slowly in response to the practical challenges faced by data practitioners based at national statistical institutes (NSIs). SDC's subsequent emergence as a specialised academic field was an outcome of three interrelated socio-technical changes: (i) the advent of accessible computing as a research tool in the 1980s meant that it became possible - and then increasingly easy - for researchers to process larger quantities of data automatically; this naturally increased demand for such data; (ii) it became possible for data holders to process and disseminate detailed data as digital files and (iii) the number of organisations holding data about individuals proliferated. This also meant the number of potential adversaries with the resources to attack any given dataset increased exponentially. In this article, we describe the state of the art for SDC and then discuss the core issues and future challenges. In particular, we touch on SDC and big data, on SDC and machine learning, and on SDC and anti-discrimination.
Physical Cryptographic Signatures for Absentee Ballots
Venue: arXiv cs.CR 2018
Year: 2018

Physical signature verification on absentee ballots became a major flashpoint in the 2018 midterm elections in the United States, especially in states like Georgia, Florida, and Arizona, where close election margins resulted in heightened attention to the counting of absentee ballots. As vote-by-mail solutions are becoming more prevalent across the U.S., these issues are sure to continue affecting elections in the United States. Signature verification is an inexact science; often times guidelines can vary widely from jurisdiction to jurisdiction. In this paper we provide a cryptographic remedy to this solution that is usable, secure, and easily integrated into existing election infrastructure.
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.
Universal Supervised Learning for Individual Data
Venue: arXiv cs.IT 2018
Year: 2018

Universal supervised learning is considered from an information theoretic point of view following the universal prediction approach, see Merhav and Feder (1998). We consider the standard supervised "batch" learning where prediction is done on a test sample once the entire training data is observed, and the individual setting where the features and labels, both in the training and test, are specific individual quantities. The information theoretic approach naturally uses the self-information loss or log-loss. Our results provide universal learning schemes that compete with a "genie" (or reference) that knows the true test label. In particular, it is demonstrated that the main proposed scheme, termed Predictive Normalized Maximum Likelihood (pNML), is a robust learning solution that outperforms the current leading approach based on Empirical Risk Minimization (ERM). Furthermore, the pNML construction provides a pointwise indication for the learnability of the specific test challenge with the given training examples
Robot Assisted Tower Construction - A Resource Distribution Task to Study Human-Robot Collaboration and Interaction with Groups of People
Venue: arXiv cs.RO 2018
Year: 2018

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should distribute resources among group members and about what the social and task related consequences of the distribution are. Methodological advances are needed to allow researchers to collect data about human robot collaboration that involves multiple people. This paper presents Tower Construction, a novel resource distribution task that allows researchers to examine collaboration between a robot and groups of people. By focusing on the question of whether and how a robot's distribution of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, we provide a case of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves multiple humans. We highlight the kinds of insights the task can yield. In particular we find that the distribution of resources affects perceptions of performance, and interpersonal dynamics between human team-members.
The algorithm of formation of a training set for an artificial neural network for image segmentation
Venue: arXiv cs.CV 2018
Year: 2018

This article suggests an algorithm of formation a training set for artificial neural network in case of image segmentation. The distinctive feature of this algorithm is that it using only one image for segmentation. The segmentation performs using three-layer perceptron. The main method of the segmentation is a method of region growing. Neural network is using for get a decision to include pixel into an area or not. Impulse noise is using for generation of a training set. Pixels damaged by noise are not related to the same region. Suggested method has been tested with help of computer experiment in automatic and interactive modes.
Hardware-in-the-Loop Co-Simulation Based Validation of Power System Control Applications
Venue: arXiv cs.SY 2018
Year: 2018

Renewables are key enablers for the realization of a sustainable energy supply but grid operators and energy utilities have to mange their intermittent behavior and limited storage capabilities by ensuring the security of supply and power quality. Advanced control approaches, automation concepts, and communication technologies have the potential to address these challenges by providing new intelligent solutions and products. However, the validation of certain aspects of such smart grid systems, especially advanced control and automation concepts is still a challenge. The main aim of this work therefore is to introduce a hardware-in-the-loop co-simulation-based validation framework which allows the simulation of large-scale power networks and control solutions together with real-world components. The application of this concept to a selected voltage control example shows its applicability.
Border Effect of Complex Network: An analysis on the cooperation network of movie stars across different regions
Venue: arXiv cs.CY 2018
Year: 2018

People always carry out social activities within certain geographic space. The geographic boundary likes national boundaries and provincial boundaries functions as dividing policies, distributing administrative resources, defining the scope of application of laws and regulations or even the boundary of cultural region, producing fundamental influence on lifestyle. Network analysis is a powerful tool to abstract complex system. In this article, we used network method to understand changes of cooperation network in movie industry and effect of geographical boundary.
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden
Venue: arXiv cs.LG 2018
Year: 2018

Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations. This can prevent the less favored people from benefiting of the advancement in ML applications for healthcare. In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results. We used in-hospital mortality prediction as our case analysis based on the MIMIC III publicly available dataset. We explored three methods on two different NN architectures. We reduced the size of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the LSTM cell allowing to use less recurrent layers for the model. Finally, we implemented quantization on DNN forcing the weights to be 8-bits instead of 32-bits. We found that all our methods increased computational efficiency without compromising accuracy and some of them even achieved higher accuracy than the pre-condensed baseline models.
Self-Localization of Parking Robots Using Square-Like Landmarks
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a framework for self-localization of parking robots in a parking lot innovatively using square-like landmarks, aiming to provide a positioning solution with low cost but high accuracy. It utilizes square structures common in parking lots such as pillars, corners or charging piles as robust landmarks and deduces the global pose of the robot in conjunction with an off-line map. The localization is performed in real-time via Particle Filter using a single line scanning LiDAR as main sensor, an odometry as secondary information sources. The system has been tested in a simulation environment built in V-REP, the result of which demonstrates its positioning accuracy below 0.20 m and a corresponding heading error below 1°.