OpenRaiser/Intern-Atlas huggingface dataset:
Learning-Based Computation Offloading for IoT Devices with Energy Harvesting
Venue: arXiv cs.NI 2017
Year: 2017

Internet of Things (IoT) devices can apply mobile-edge computing (MEC) and energy harvesting (EH) to provide the satisfactory quality of experiences for computation intensive applications and prolong the battery lifetime. In this article, we investigate the computation offloading for IoT devices with energy harvesting in wireless networks with multiple MEC devices such as base stations and access points, each with different computation resource and radio communication capability. We propose a reinforcement learning based computation offloading framework for an IoT device to choose the MEC device and determine the offloading rate according to the current battery level, the previous radio bandwidth to each MEC device and the predicted amount of the harvested energy. A "hotbooting" Q-learning based computation offloading scheme is proposed for an IoT device to achieve the optimal offloading performance without being aware of the MEC model, the energy consumption and computation latency model. We also propose a fast deep Q-network (DQN) based offloading scheme, which combines the deep learning and hotbooting techniques to accelerate the learning speed of Q-learning. We show that the proposed schemes can achieve the optimal offloading policy after sufficiently long learning time and provide their performance bounds under two typical MEC scenarios. Simulations are performed for IoT devices that use wireless power transfer to capture the ambient radio-frequency signals to charge the IoT batteries. Simulation results show that the fast DQN-based offloading scheme reduces the energy consumption, decreases the computation delay and the task drop ratio, and increases the utility of the IoT device in dynamic MEC, compared with the benchmark Q-learning based offloading.
Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation
Venue: arXiv cs.CL 2017
Year: 2017

We investigate whether infant-directed speech (IDS) could facilitate word form learning when compared to adult-directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable in IDS than in ADS. At the phonological level, we find an effect in the opposite direction: the IDS lexicon contains more distinctive words (such as onomatopoeias) than the ADS counterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result, IDS word forms are still globally less discriminable than ADS word forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role of IDS is to improve language learnability.
Use of Generative Adversarial Network for Cross-Domain Change Detection
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the problem of cross-domain change detection from a novel perspective of image-to-image translation. In general, change detection aims to identify interesting changes between a given query image and a reference image of the same scene taken at a different time. This problem becomes a challenging one when query and reference images involve different domains (e.g., time of the day, weather, and season) due to variations in object appearance and a limited amount of training examples. In this study, we address the above issue by leveraging a generative adversarial network (GAN). Our key concept is to use a limited amount of training data to train a GAN-based image translator that maps a reference image to a virtual image that cannot be discriminated from query domain images. This enables us to treat the cross-domain change detection task as an in-domain image comparison. This allows us to leverage the large body of literature on in-domain generic change detectors. In addition, we also consider the use of visual place recognition as a method for mining more appropriate reference images over the space of virtual images. Experiments validate efficacy of the proposed approach.
Lectures on Randomized Numerical Linear Algebra
Venue: arXiv cs.DS 2017
Year: 2017

This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data.
Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
Venue: arXiv cs.AI 2017
Year: 2017

A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site. The Pearson correlation coefficient is adopted to rank the input variables (traffic flows) for prediction, and the best-first strategy is employed to select a subset as the cause nodes of a Bayesian network. Given the derived cause nodes and the corresponding effect node in the spatio-temporal Bayesian network, a Gaussian Mixture Model is applied to describe the statistical relationship between the input and output. Finally, traffic flow forecasting is performed under the criterion of Minimum Mean Square Error (M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing demonstrate the effectiveness of our presented spatio-temporal Bayesian network predictor.
On tractable query evaluation for SPARQL
Venue: arXiv cs.CC 2017
Year: 2017

Despite much work within the last decade on foundational properties of SPARQL - the standard query language for RDF data - rather little is known about the exact limits of tractability for this language. In particular, this is the case for SPARQL queries that contain the OPTIONAL-operator, even though it is one of the most intensively studied features of SPARQL. The aim of our work is to provide a more thorough picture of tractable classes of SPARQL queries. In general, SPARQL query evaluation is PSPACE-complete in combined complexity, and it remains PSPACE-hard already for queries containing only the OPTIONAL-operator. To amend this situation, research has focused on "well-designed SPARQL queries" and their recent generalization "weakly well-designed SPARQL queries". For these two fragments the evaluation problem is coNP-complete in the absence of projection and SigmaP2-complete otherwise. Moreover, they have been shown to contain most SPARQL queries asked in practical settings. In this paper, we study tractable classes of weakly well-designed queries in parameterized complexity considering the equivalent formulation as pattern trees. We give a complete characterization of the tractable classes in the case without projection. Moreover, we show a characterization of all tractable classes of simple well-designed pattern trees in the presence of projection.
Towards Profit Maximization for Online Social Network Providers
Venue: arXiv cs.SI 2017
Year: 2017

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.
How do you Command an Army of Intelligent Things?
Venue: arXiv cs.CY 2017
Year: 2017

Within a decade, probably less, we will need to find ways to work effectively with ever growing numbers of intelligent things, including robots and intelligent agents. The networked workforce of the near future will thus consist of not only interconnected and interdependent humans but also of intelligent things. This raises a number of challenging issues, none more compelling and urgent than finding an answer to the question "How to manage this new organizational form?" We consider these issues in a particularly challenging domain of human endeavor -- warfare. Command and Control (C2) is the term applied to management or governance of military organizations and endeavors. We consider how human and other intelligent entities can best contribute to ensuring that the decision makers, whether human or machine, have the information they require and make good use of this information to accomplish C2 functions. Commanders or managers of mixed human-thing organizations will face several challenges that the discussion above has highlighted. Things are challenged in a number of areas and will need humans to provide these capabilities. These include their ability to explain, build trust, bond, understand personal agendas, emotions, politics, and negotiate. Things and people both to some extent have difficulty anticipating and coping with the unusual and unexpected and to think of out-of-the-box solutions.
Android Malware Detection using Deep Learning on API Method Sequences
Venue: arXiv cs.CR 2017
Year: 2017

Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1K to 33K malware apps, and 38K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%-99% and a false positive rate of 0.06%-2%, under all tested datasets and settings.
Mobile Phone Based Portable Field Sensor System for Real-Time In-situ River Water Quality Monitoring During Endangered Dolphin Monitoring Surveys
Venue: arXiv cs.CY 2017
Year: 2017

Mobile phone based potable water quality assessment device is developed to analyze and study water pollution level at Indus river. Indus river is habitat of endangered Indus river dolphin and water pollution is one of major causes of survivability threats for this specie. We tested device performance at the six locations of Lahore canal. pH of canal water deviates from the normal range of the irrigation water. In future, we will study correlation between water pollution level and habitat usage of Indus river dolphin using water quality assessment device and hydrophone array based passive acoustic monitoring (PAM) system.
PWCT: Visual Language for IoT and Cloud Computing Applications and Systems
Venue: arXiv cs.PL 2017
Year: 2017

Developing IoT, Data Computing and Cloud Computing software requires different programming skills and different programming languages. This cause a problem for many companies and researchers that need to hires many programmers to develop a complete solution. The problem is related directly to the financial cost and the development time which are very important factors to many research projects. In this paper we present and propose the PWCT visual programming tool for developing IoT, Data Computing and Cloud Computing Applications and Systems without writing textual code directly. Using PWCT increase productivity and provide researchers with one visual programming tool to develop different solutions.
SoA-Fog: Secure Service-Oriented Edge Computing Architecture for Smart Health Big Data Analytics
Venue: arXiv cs.DC 2017
Year: 2017

The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In this paper, we proposed a secure serviceorientated edge computing architecture that is validated on recently released public dataset. Results and discussions support the applicability of proposed architecture for smart health applications. We proposed SoA-Fog i.e. a three-tier secure framework for efficient management of health data using fog devices. It discuss the security aspects in client layer, fog layer and the cloud layer. We design the prototype by using win-win spiral model with use case and sequence diagram. Overlay analysis was performed using proposed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014. The mobile clients were taken as test case. We performed comparative analysis between proposed secure fog framework and state-of-the art cloud-based framework.
Computer Algebra Methods in Control Systems
Venue: arXiv cs.SY 2017
Year: 2017

As dynamic and control systems become more complex, relying purely on numerical computations for systems analysis and design might become extremely expensive or totally infeasible. Computer algebra can act as an enabler for analysis and design of such complex systems. It also provides means for characterization of all solutions and studying them before realizing a particular solution. This note provides a brief survey on some of the applications of symbolic computations in control systems analysis and design.
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
Venue: arXiv cs.DC 2017
Year: 2017

We explore scaling of the standard distributed Tensorflow with GRPC primitives on up to 512 Intel Xeon Phi (KNL) nodes of Cori supercomputer with synchronous stochastic gradient descent (SGD), and identify causes of scaling inefficiency at higher node counts. To our knowledge, this is the first exploration of distributed GRPC Tensorflow scalability on a HPC supercomputer at such large scale with synchronous SGD. We studied scaling of two convolution neural networks - ResNet-50, a state-of-the-art deep network for classification with roughly 25.5 million parameters, and HEP-CNN, a shallow topology with less than 1 million parameters for common scientific usages. For ResNet-50, we achieve >80% scaling efficiency on up to 128 workers, using 32 parameter servers (PS tasks) with a steep decline down to 23% for 512 workers using 64 PS tasks. Our analysis of the efficiency drop points to low network bandwidth utilization due to combined effect of three factors. (a) Heterogeneous distributed parallelization algorithm which uses PS tasks as centralized servers for gradient averaging is suboptimal for utilizing interconnect bandwidth. (b) Load imbalance among PS tasks hinders their efficient scaling. (c) Underlying communication primitive GRPC is currently inefficient on Cori high-speed interconnect. The HEP-CNN demands less interconnect bandwidth, and shows >80% weak scaling efficiency for up to 256 nodes with only 1 PS task. Our findings are applicable to other deep learning networks. Big networks with millions of parameters stumble upon the issues discussed here. Shallower networks like HEP-CNN with relatively lower number of parameters can efficiently enjoy weak scaling even with a single parameter server.
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
Venue: arXiv cs.CV 2017
Year: 2017

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet
Effective Optimization Criteria and Relay Selection Algorithms for Physical-Layer Security in Multiple-Antenna Relay Networks
Venue: arXiv cs.IT 2017
Year: 2017

Physical-layer security for wireless networks has become an effective approach and recently drawn significant attention in the literature. In particular, the deployment and allocation of resources such as relays to assist the transmission have gained significant interest due to their ability to improve the secrecy rate of wireless networks. In this work, we examine relay selection criteria with arbitrary knowledge of the channels of the users and the eavesdroppers. We present alternative optimization criteria based on the signal-to-interference and the secrecy rate criteria that can be used for resource allocation and that do not require knowledge of the channels of the eavesdroppers and the interference. We then develop effective relay selection algorithms that can achieve a high secrecy rate performance without the need for the knowledge of the channels of the eavesdroppers and the interference. Simulation results show that the proposed criteria and algorithms achieve excellent performance.
The L-CSC cluster: greenest supercomputer in the world in Green500 list of November 2014
Venue: arXiv cs.PF 2017
Year: 2017

The L-CSC (Lattice Computer for Scientific Computing) is a general purpose compute cluster built of commodity hardware installed at GSI. Its main operational purpose is Lattice QCD (LQCD) calculations for physics simulations. Quantum Chromo Dynamics (QCD) is the physical theory describing the strong force, one of the four known fundamental interactions in the universe. L-CSC leverages a multi-GPU design accommodating the huge demand of LQCD for memory bandwidth. In recent years, heterogeneous clusters with accelerators such as GPUs have become more and more powerful while supercomputers in general have shown enormous increases in power consumption making electricity costs and cooling a significant factor in the total cost of ownership. Using mainly GPUs for processing, L-CSC is very power efficient, and its architecture was optimized to provide the greatest possible power efficiency. This paper presents the cluster design as well as optimizations to improve the power efficiency. It examines the power measurements performed for the Green500 list of the most power efficient supercomputers in the world which led to the number 1 position as the greenest supercomputer in November 2014.
User-Centric Joint Transmission in Virtual-Cell-Based Ultra-Dense Networks
Venue: arXiv cs.NI 2017
Year: 2017

In ultra-dense networks (UDNs), distributed radio access points (RAPs) are configured into small virtual cells around mobile users for fair and high-throughput services. In this correspondence, we evaluate the performance of user-centric joint transmission (JT) in a UDN with a number of virtual cells. In contrast to existing cooperation schemes, which assume constant RAP transmit power, we consider a total transmit power constraint for each user, and assume that the total power is optimally allocated to the RAPs in each virtual cell using maximum ratio transmission (MRT). Based on stochastic geometry models of the RAP and user locations, we resolve the correlation of transmit powers introduced by MRT and derive the average user throughput. Numerical results show that user-centric JT with MRT provides a high signal-to-noise ratio (SNR) without generating severe interference to other co-channel users. Moreover, we show that MRT precoding, while requiring channel-state-information (CSI), is essential for the success of JT.
A Gap-Based Framework for Chinese Word Segmentation via Very Deep Convolutional Networks
Venue: arXiv cs.CL 2017
Year: 2017

Most previous approaches to Chinese word segmentation can be roughly classified into character-based and word-based methods. The former regards this task as a sequence-labeling problem, while the latter directly segments character sequence into words. However, if we consider segmenting a given sentence, the most intuitive idea is to predict whether to segment for each gap between two consecutive characters, which in comparison makes previous approaches seem too complex. Therefore, in this paper, we propose a gap-based framework to implement this intuitive idea. Moreover, very deep convolutional neural networks, namely, ResNets and DenseNets, are exploited in our experiments. Results show that our approach outperforms the best character-based and word-based methods on 5 benchmarks, without any further post-processing module (e.g. Conditional Random Fields) nor beam search.
A Fast and Accurate Failure Frequency Approximation for $k$-Terminal Reliability Systems
Venue: arXiv cs.DS 2017
Year: 2017

This paper considers the problem of approximating the failure frequency of large-scale composite $k$-terminal reliability systems. In such systems, the nodes ($k$ of which are terminals) are connected through components which are subject to random failure and repair processes. At any time, a system failure occurs if the surviving system fails to connect all the k terminals together. We assume that each component's up-times and down-times follow statistically independent stationary random processes, and these processes are statistically independent across the components. In this setting, the exact computation of failure frequency is known to be computationally intractable (NP-hard). In this work, we present an algorithm to approximate the failure frequency for any given multiplicative error factor that runs in polynomial time in the number of (minimal) cutsets. Moreover, for the special case of all-terminal reliability systems, i.e., where all nodes are terminals, we propose an algorithm for approximating the failure frequency within an arbitrary multiplicative error that runs in polynomial time in the number of nodes (which can be much smaller than the number of cutsets). In addition, our simulation results confirm that the proposed method is much faster and more accurate than the Monte Carlo simulation technique for approximating the failure frequency.