OpenRaiser/Intern-Atlas huggingface dataset:
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
Venue: arXiv cs.CV 2018
Year: 2018

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.
Secure and Efficiently Searchable IoT Communication Data Management Model: Using Blockchain as a new tool
Venue: arXiv cs.CR 2018
Year: 2018

With the rapid development of the Internet of things (IoT), more and more IoT devices are connected and communicate frequently. In this background, the traditional centralized security architecture of IoT will be limited in terms of data storage space, data reliability, scalability, operating costs and liability judgment. In this paper, we propose an new key information storage framework based on a small distributed database generated by blockchain technology and cloud storage. Specifically, all encrypted key communication data will be upload to public could server for enough storage, but the abstracts of these data (called "communication logs") will be recorded in "IoT ledger" (i.e., an distributed database) that maintained by all IoT devices according to the blockchain generation approach, which could solve the problem of data reliability, scalability and liability judgment. Besides, in order to efficiently search communication logs and not reveal any sensitive information of communication data, we design the secure search scheme for our "IoT ledger", which exploits the Asymmetric Scalar-product Preserving Encryption (ASPE) approach to guarantee the data security, and exploits the 2-layers index which is tailor-made for blockchain database to improve the search efficiency. Security analysis and experiments on synthetic dataset show that our schemes are secure and efficient.
Design of Energy-efficient EPON: a Novel Protocol Proposal and its Performance Analysis
Venue: arXiv cs.NI 2018
Year: 2018

Economical and environmental concerns necessitate network engineers to focus on energy-efficient access network design. The optical network units (ONUs), being predominantly responsible for the energy consumption of Ethernet Passive Optical Network (EPON), motivates us towards designing a novel protocol for saving energy at ONU. The proposed protocol exploits different low power modes (LPM) and opts for the suitable one using traffic prediction. This scheme provides a significant improvement of energy-efficiency especially at high load (~ 40%) over existing protocols. A better understanding of the performance and a deeper insight into several design aspects can only be addressed through a detailed mathematical analysis. The proposed protocol involves traffic prediction which infringes Markovian property. However, some pragmatic assumptions along with a proper selection of observation instances and state descriptions allow us to form a Discrete Time Markov Chain (DTMC) of the proposed algorithm. Thus, the primary objective of this paper is to propose a novel scheme for achieving energy-efficiency at ONU and mathematically analyze the performance of it with the help of a DTMC. The analysis reveals that the energy-efficiency is more sensitive to the power consumption of doze mode as compared to other LPM while the effect of sleep-to-wake-up time is minor.
A Fuzzy System based Approach to Extend Network Lifetime for En-Route Filtering Schemes in WSNs
Venue: arXiv cs.NI 2018
Year: 2018

Wireless sensor networks suffer from false report injection attacks. This results in energy drain over sensor nodes on the event traversal route. Novel en-route filtering schemes counter this problem by filtering these attacks on designated verification nodes. However, these filtering schemes among other limitations inherently are network lifetime inefficient. Generally, report traversal paths and verification nodes are also fixed. In this paper, we cater these limitations in our proposed scheme. Simulation experiments results show that proposed schemes outperforms existing en-route filtering schemes in networks lifetime. We employed a Fuzzy Logic System to select forwarding nodes from candidate nodes based on current network conditions. Proposed scheme gains in network lifetime, and energy-efficiency while having comparable false report filtering efficiency.
Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program
Venue: arXiv cs.CY 2018
Year: 2018

Over the past two decades, High-Performance Computing (HPC) communities have developed many models for delivering education aiming to help students understand and harness the power of parallel and distributed computing. Most of these courses either lack a hands-on component or heavily focus on theoretical characterization behind complex algorithms. To bridge the gap between application and scientific theory, NVIDIA Deep Learning Institute (DLI) (www.nvidia.com/dli) has designed an on-line education and training platform that helps students, developers, and engineers solve real-world problems in a wide range of domains using deep learning and accelerated computing. DLI's accelerated computing course content starts with the fundamentals of accelerating applications with CUDA and OpenACC in addition to other courses in training and deploying neural networks for deep learning. Advanced and domain-specific courses in deep learning are also available. The online platform enables students to use the latest AI frameworks, SDKs, and GPU-accelerated technologies on fully-configured GPU servers in the cloud so the focus is more on learning and less on environment setup. Students are offered project-based assessment and certification at the end of some courses. To support academics and university researchers teaching accelerated computing and deep learning, the DLI University Ambassador Program enables educators to teach free DLI courses to university students, faculty, and researchers.
Automatic Quality Assurance and Release (Report from Dagstuhl Seminar 18122)
Venue: arXiv cs.SE 2018
Year: 2018

This report documents the program and the outcomes of Dagstuhl Seminar 18122 "Automatic Quality Assurance and Release". The main goal of this seminar was to bridge the knowledge divide on how researchers and industry professionals reason about and implement DevOps for automatic quality assurance. Through the seminar, we have built up a common understanding of DevOps tools and practices, but we have also identified major academic and educational challenges for this field of research.
A General Approach to Domain Adaptation with Applications in Astronomy
Venue: arXiv cs.LG 2018
Year: 2018

The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications. The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario. An example is to build a predictive model on spectroscopic data to identify Supernovae IA, while subsequently trying to adapt such model on photometric data. In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation. Results using two real astronomical problems, Supernova Ia classification and identification of Mars landforms, show two main advantages with our approach: increased accuracy performance and substantial savings in computational cost.
Software Reliability Growth Models Predict Autonomous Vehicle Disengagement Events
Venue: arXiv cs.SE 2018
Year: 2018

The acceptance of autonomous vehicles is dependent on the rigorous assessment of their safety. Furthermore, the commercial viability of AV programs depends on the ability to estimate the time and resources required to achieve desired safety levels. Naive approaches to estimating the reliability and safety levels of autonomous vehicles under development are will require infeasible amounts of testing of a static vehicle configuration. To permit both the estimation of current safety, and make predictions about the reliability of future systems, I propose the use of a standard tool for modelling the reliability of evolving software systems, software reliability growth models (SRGMs). Publicly available data from Californian public-road testing of two autonomous vehicle systems is modelled using two of the best-known SRGMs. The ability of the models to accurately estimate current reliability, as well as for current testing data to predict reliability in the future after additional testing, is evaluated. One of the models, the Musa-Okumoto model, appears to be a good estimator and a reasonable predictor.
Primal path algorithm for compositional data analysis
Venue: arXiv cs.LG 2018
Year: 2018

Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data.
A Construction of Optimal Frequency Hopping Sequence Set via Combination of Multiplicative and Additive Groups of Finite Fields
Venue: arXiv cs.IT 2018
Year: 2018

In literatures, there are various constructions of frequency hopping sequence (FHS for short) sets with good Hamming correlations. Some papers employed only multiplicative groups of finite fields to construct FHS sets, while other papers implicitly used only additive groups of finite fields for construction of FHS sets. In this paper, we make use of both multiplicative and additive groups of finite fields simultaneously to present a construction of optimal FHS sets. The construction provides a new family of optimal $\left(q^m-1,\frac{q^{m-t}-1}{r},rq^t;\frac{q^{m-t}-1}{r}+1\right)$ frequency hopping sequence sets archiving the Peng-Fan bound. Thus, the FHS sets constructed in literatures using either multiplicative groups or additive groups of finite fields are all included in our family. In addition, some other FHS sets can be obtained via the well-known recursive constructions through one-coincidence sequence set.
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
Venue: arXiv cs.NI 2018
Year: 2018

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency.
An Overview on Data Security in Cloud Computing
Venue: arXiv cs.CR 2018
Year: 2018

Cloud Computing refers to the use of computer resources as a service on-demand via internet. It is mainly based on data and applications outsourcing, traditionally stored on users' computers, to remote servers (datacenters) owned, administered and managed by third parts. This paper is an overview of data security issues in the cloud computing. Its objective is to highlight the principal issues related to data security that raised by cloud environment. To do this, these issues was classified into three categories: 1-data security issues raised by single cloud characteristics compared to traditional infrastructure, 2-data security issues raised by data life cycle in cloud computing (stored, used and transferred data), 3-data security issues associated to data security attributes (confidentiality, integrity and availability). For each category, the common solutions used to secure data in the cloud were emphasized.
A Smart Security System with Face Recognition
Venue: arXiv cs.MM 2018
Year: 2018

Web-based technology has improved drastically in the past decade. As a result, security technology has become a major help to protect our daily life. In this paper, we propose a robust security based on face recognition system (SoF). In particular, we develop this system to giving access into a home for authenticated users. The classifier is trained by using a new adaptive learning method. The training data are initially collected from social networks. The accuracy of the classifier is incrementally improved as the user starts using the system. A novel method has been introduced to improve the classifier model by human interaction and social media. By using a deep learning framework - TensorFlow, it will be easy to reuse the framework to adopt with many devices and applications.
The Technology Transfer of the Italian Public Research System: the Case of the National Research Council of Italy
Venue: arXiv cs.DL 2018
Year: 2018

This paper deals with the technology transfer activities of the main public research institution in Italy, the Italian Research Council, CNR. A comparative analysis on patenting and licensing performances between CNR and the Massachusetts Institute of Technology, MIT has been carried out. Findings show that: research expenses being equal, CNR patents are 26% of MIT's; and, patents being equal, CNR licenses are also 26% of MIT's. This means that CNR impact on domestic competitiveness, in terms of patent licenses, is less than 7% of MIT's. Moreover, while 83% of CNR patents are never licensed to domestic industry, the Italian technology balance of payments shows a perennial deficit. The paper concludes with the identification of the possible causes that may explain such a gap.
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process
Venue: arXiv cs.LG 2018
Year: 2018

We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines. The three-dimensional force data streams we used were extracted from a turning experiment of 21 runs for which a tool was run until it generally satisfied an end-of-life criterion. Our real-time anomaly detection algorithm was scored and optimised according to how precisely it can predict the progressive wear of the tool flank. Most of our tool wear predictions were accurate and reliable as illustrated in our off-line simulation results. Particularly when the multivariate analysis was applied, the algorithm we develop was found to be very robust across different scenarios and against parameter changes. It shall be reasonably easy to apply our approach elsewhere for real-time tool wear analytics.
Complexity of Partitioning Hypergraphs
Venue: arXiv cs.CC 2018
Year: 2018

For a given $π=(π_0, π_1,..., π_k) \in \{0, 1, *\}^{k+1}$, we want to determine whether an input $k$-uniform hypergraph $G=(V, E)$ has a partition $(V_1, V_2)$ of the vertex set so that for all $X \subseteq V$ of size $k$, $X \in E$ if $π_{|X\cap V_1|}=1$ and $X \notin E$ if $π_{|X\cap V_1|}=0$. We prove that this problem is either polynomial-time solvable or NP-complete depending on $π$ when $k=3$ or $4$. We also extend this result into $k$-uniform hypergraphs for $k \geq 5$.
Solution Dominance over Constraint Satisfaction Problems
Venue: arXiv cs.AI 2018
Year: 2018

Constraint Satisfaction Problems (CSPs) typically have many solutions that satisfy all constraints. Often though, some solutions are preferred over others, that is, some solutions dominate other solutions. We present solution dominance as a formal framework to reason about such settings. We define Constraint Dominance Problems (CDPs) as CSPs with a dominance relation, that is, a preorder over the solutions of the CSP. This framework captures many well-known variants of constraint satisfaction, including optimization, multi-objective optimization, Max-CSP, minimal models, minimum correction subsets as well as optimization over CP-nets and arbitrary dominance relations. We extend MiniZinc, a declarative language for modeling CSPs, to CDPs by introducing dominance nogoods; these can be derived from dominance relations in a principled way. A generic method for solving arbitrary CDPs incrementally calls a CSP solver and is compatible with any existing solver that supports MiniZinc. This encourages experimenting with different solution dominance relations for a problem, as well as comparing different solvers without having to modify their implementations.
Wikipedia Text Reuse: Within and Without
Venue: arXiv cs.IR 2018
Year: 2018

We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws due to inconsistencies arising from asynchronous editing of reused passages, or complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia's influence on the web. To foster future research into these tasks, and for reproducibility's sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this project, we worked on speech recognition, specifically predicting individual words based on both the video frames and audio. Empowered by convolutional neural networks, the recent speech recognition and lip reading models are comparable to human level performance. We re-implemented and made derivations of the state-of-the-art model. Then, we conducted rich experiments including the effectiveness of attention mechanism, more accurate residual network as the backbone with pre-trained weights and the sensitivity of our model with respect to audio input with/without noise.
A local search $4/3$-approximation algorithm for the minimum $3$-path partition problem
Venue: arXiv cs.DS 2018
Year: 2018

Given a graph $G = (V, E)$, the $3$-path partition problem is to find a minimum collection of vertex-disjoint paths each of order at most $3$ to cover all the vertices of $V$. It is different from but closely related to the well-known $3$-set cover problem. The best known approximation algorithm for the $3$-path partition problem was proposed recently and has a ratio $13/9$. Here we present a local search algorithm and show, by an amortized analysis, that it is a $4/3$-approximation. This ratio matches up to the best approximation ratio for the $3$-set cover problem.
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
Venue: arXiv cs.CV 2018
Year: 2018

The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically limited by the optics and sensor characteristics. In addition, practical and economic reasons often stipulate the use of out-dated or low-cost hardware. Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. This work covers novel super-resolution methods along with new applications in medical imaging.
A Fuzzy Community-Based Recommender System Using PageRank
Venue: arXiv cs.IR 2018
Year: 2018

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.