OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Revenue Cost Dynamics in Large Data Centers: A Factorial Design Approach
Venue: arXiv cs.DC 2016
Year: 2016

Revenue optimization of large data centers is an open and challenging problem. The intricacy of the problem is due to the presence of too many parameters posing as costs or investment. This paper proposes a model to optimize the revenue in cloud data center and analyzes the model, revenue and different investment or cost commitments of organizations investing in data centers. The model uses the Cobb-Douglas production function to quantify the boundaries and the most significant factors to generate the revenue. The dynamics between revenue and cost is explored by designing an experiment (DoE) which is an interpretation of revenue as function of cost/investment as factors with different levels/fluctuations. Optimal elasticities associated with these factors of the model for maximum revenue are computed and verified . The model response is interpreted in light of the business scenario of data centers.
Review of Buffer-Aided Distributed Space-Time Coding Schemes and Algorithms for Cooperative Wireless Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we propose buffer-aided distributed space-time coding (DSTC) schemes and relay selection algorithms for cooperative direct-sequence code-division multiple access (DS-CDMA) systems. We first devise a relay pair selection algorithm that can form relay pairs and then select the optimum set of relays among both the source-relay phase and the relay-destination phase according to the signal-to-interference-plus-noise ratio (SINR) criterion. Multiple relays equipped with dynamic buffers are then introduced in the network, which allows the relays to store data received from the sources and wait until the most appropriate time for transmission. { A greedy relay pair selection algorithm is then developed to reduce the high cost of the exhaustive search required when a large number of relays are involved.} The proposed techniques effectively improve the quality of the transmission with an acceptable delay as the buffer size is adjustable. An analysis of the computational complexity of the proposed algorithms, the delay and a study of the greedy algorithm are then carried out. Simulation results show that the proposed dynamic buffer-aided DSTC schemes and algorithms outperform prior art.
Faster Kernels for Graphs with Continuous Attributes via Hashing
Venue: arXiv cs.LG 2016
Year: 2016

While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones. The idea is to iteratively turn continuous attributes into discrete labels using randomized hash functions. We illustrate hash graph kernels for the Weisfeiler-Lehman subtree kernel and for the shortest-path kernel. The resulting novel graph kernels are shown to be, both, able to handle graphs with continuous attributes and scalable to large graphs and data sets. This is supported by our theoretical analysis and demonstrated by an extensive experimental evaluation.
Auditing Australian Senate Ballots
Venue: arXiv cs.CR 2016
Year: 2016

We explain why the Australian Electoral Commission should perform an audit of the paper Senate ballots against the published preference data files. We suggest four different post-election audit methods appropriate for Australian Senate elections. We have developed prototype code for all of them and tested it on preference data from the 2016 election.
Data-Unit-Size Distribution Model with Retransmitted Packet Size Preservation Property and Its Application to Goodput Analysis for Stop-and-Wait Protocol: Case of Independent Packet Losses
Venue: arXiv cs.PF 2016
Year: 2016

This paper proposes a data-unit-size distribution model to represent the retransmitted packet size preservation (RPSP) property in a scenario where independently lost packets are retransmitted by a stop-and-wait protocol. RPSP means that retransmitted packets with the same sequence number are equal in size to the packet of the original transmission, which is identical to the packet generated from a message through the segmentation function, namely, generated packet. Furthermore, we derive goodput formula using an approach to derive the data-unit-size distribution. We investigate the effect of RPSP on frame size distributions and goodput in a simple case when no collision happens over the bit-error prone wireless network equipped with IEEE 802.11 Distributed Coordination Function, which is a typical example of the stop-and-wait protocol. Numerical results show that the effect gets stronger as bit error rate increases and the maximum size of the generated packets is larger than the mean size for large enough packet retry limits because longer packets will be repeatedly corrupted and retransmitted more times as a result of RPSP.
Near-Infrared Image Dehazing Via Color Regularization
Venue: arXiv cs.CV 2016
Year: 2016

Near-infrared imaging can capture haze-free near-infrared gray images and visible color images, according to physical scattering models, e.g., Rayleigh or Mie models. However, there exist serious discrepancies in brightness and image structures between the near-infrared gray images and the visible color images. The direct use of the near-infrared gray images brings about another color distortion problem in the dehazed images. Therefore, the color distortion should also be considered for near-infrared dehazing. To reflect this point, this paper presents an approach of adding a new color regularization to conventional dehazing framework. The proposed color regularization can model the color prior for unknown haze-free images from two captured images. Thus, natural-looking colors and fine details can be induced on the dehazed images. The experimental results show that the proposed color regularization model can help remove the color distortion and the haze at the same time. Also, the effectiveness of the proposed color regularization is verified by comparing with other conventional regularizations. It is also shown that the proposed color regularization can remove the edge artifacts which arise from the use of the conventional dark prior model.
Battling the Digital Forensic Backlog through Data Deduplication
Venue: arXiv cs.CY 2016
Year: 2016

In everyday life. Technological advancement can be found in many facets of life, including personal computers, mobile devices, wearables, cloud services, video gaming, web-powered messaging, social media, Internet-connected devices, etc. This technological influence has resulted in these technologies being employed by criminals to conduct a range of crimes -- both online and offline. Both the number of cases requiring digital forensic analysis and the sheer volume of information to be processed in each case has increased rapidly in recent years. As a result, the requirement for digital forensic investigation has ballooned, and law enforcement agencies throughout the world are scrambling to address this demand. While more and more members of law enforcement are being trained to perform the required investigations, the supply is not keeping up with the demand. Current digital forensic techniques are arduously time-consuming and require a significant amount of man power to execute. This paper discusses a novel solution to combat the digital forensic backlog. This solution leverages a deduplication-based paradigm to eliminate the reacquisition, redundant storage, and reanalysis of previously processed data.
Very Deep Convolutional Neural Networks for Robust Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora 4 task. The appropriate number of convolutional layers, the sizes of the filters, pooling operations and input feature maps are all modified: the filter and pooling sizes are reduced and dimensions of input feature maps are extended to allow adding more convolutional layers. Furthermore appropriate input padding and input feature map selection strategies are developed. In addition, an adaptation framework using joint training of very deep CNN with auxiliary features i-vector and fMLLR features is developed. These modifications give substantial word error rate reductions over the standard CNN used as baseline. Finally the very deep CNN is combined with an LSTM-RNN acoustic model and it is shown that state-level weighted log likelihood score combination in a joint acoustic model decoding scheme is very effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%, further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN joint decoding.
Flocking Virtual Machines in Quest for Responsive IoT Cloud Services
Venue: arXiv cs.NI 2016
Year: 2016

We propose Flock; a simple and scalable protocol that enables live migration of Virtual Machines (VMs) across heterogeneous edge and conventional cloud platforms to improve the responsiveness of cloud services. Flock is designed with properties that are suitable for the use cases of the Internet of Things (IoT). We describe the properties of regularized latency measurements that Flock can use for asynchronous and autonomous migration decisions. Such decisions allow communicating VMs to follow a flocking-like behavior that consists of three simple rules: separation, alignment, and cohesion. Using game theory, we derive analytical bounds on Flock's Price of Anarchy (PoA), and prove that flocking VMs converge to a Nash Equilibrium while settling in the best possible cloud platforms. We verify the effectiveness of Flock through simulations and discuss how its generic objective can simply be tweaked to achieve other objectives, such as cloud load balancing and energy consumption minimization.
Polynomial Time Algorithms for Bichromatic Problems
Venue: arXiv cs.CG 2016
Year: 2016

In this article, we consider a collection of geometric problems involving points colored by two colors (red and blue), referred to as bichromatic problems. The motivation behind studying these problems is two fold; (i) these problems appear naturally and frequently in the fields like Machine learning, Data mining, and so on, and (ii) we are interested in extending the algorithms and techniques for single point set (monochromatic) problems to bichromatic case. For all the problems considered in this paper, we design low polynomial time exact algorithms. These algorithms are based on novel techniques which might be of independent interest.
Stacked Autoencoders for Medical Image Search
Venue: arXiv cs.CV 2016
Year: 2016

Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collection of 14,410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark, it was found that stacked autoencoders gave excellent results with a retrieval error of 376 for 1,733 test images with a compression of 74.61%.
Rain structure transfer using an exemplar rain image for synthetic rain image generation
Venue: arXiv cs.CV 2016
Year: 2016

This letter proposes a simple method of transferring rain structures of a given exemplar rain image into a target image. Given the exemplar rain image and its corresponding masked rain image, rain patches including rain structures are extracted randomly, and then residual rain patches are obtained by subtracting those rain patches from their mean patches. Next, residual rain patches are selected randomly, and then added to the given target image along a raster scanning direction. To decrease boundary artifacts around the added patches on the target image, minimum error boundary cuts are found using dynamic programming, and then blending is conducted between overlapping patches. Our experiment shows that the proposed method can generate realistic rain images that have similar rain structures in the exemplar images. Moreover, it is expected that the proposed method can be used for rain removal. More specifically, natural images and synthetic rain images generated via the proposed method can be used to learn classifiers, for example, deep neural networks, in a supervised manner.
Wireless Authentication System for Barcode Scanning Using Infrared Communication Technique
Venue: arXiv cs.CR 2016
Year: 2016

This paper presents the verified methodology for wireless authentication system using infrared barcode based scanner. An alternate approach of scanning a barcode using an infrared communication is implemented here, comprises of a card on which a barcode is printed is illuminated with an infrared beam. The reflections from the card are received by an infrared receiver and transmit them to a remote machine over wireless channel where the retrieve information is further processed and converted into the corresponding barcode digits which are further matched against the database stored in the server and the authenticity of the user is established. The system developed is cost-effective, efficient, provide double security and robust.
Source Accountability with Domain-brokered Privacy
Venue: arXiv cs.NI 2016
Year: 2016

In an ideal network, every packet would be attributable to its sender, while host identities and transmitted content would remain private. Designing such a network is challenging because source accountability and communication privacy are typically viewed as conflicting properties. In this paper, we propose an architecture that guarantees source accountability and privacy-preserving communication by enlisting ISPs as accountability agents and privacy brokers. While ISPs can link every packet in their network to their customers, customer identity remains unknown to the rest of the Internet. In our architecture, network communication is based on Ephemeral Identifiers (EphIDs)---cryptographic tokens that can be linked to a source only by the source's ISP. We demonstrate that EphIDs can be generated and processed efficiently, and we analyze the practical considerations for deployment.
Pooling Hybrid Representations for Web Structured Data Annotation
Venue: arXiv cs.DB 2016
Year: 2016

Automatically identifying data types of web structured data is a key step in the process of web data integration. Web structured data is usually associated with entities or objects in a particular domain. In this paper, we aim to map attributes of an entity in a given domain to pre-specified classes of attributes in the same domain based on their values. To perform this task, we propose a hybrid deep learning network that relies on the format of the attributes' values. It does so without any pre-processing or using pre-defined hand-crafted features. The hybrid network combines sequence-based neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), to learn the sequence structure of attributes' values. The CNN captures short-distance dependencies in these sequences through a sliding window approach, and the RNN captures long-distance dependencies by storing information of previous characters. These networks create different vector representations of the input sequence which are combined using a pooling layer. This layer applies a specific operation on these vectors in order to capture their most useful patterns for the task. Finally, on top of the pooling layer, a softmax function predicts the label of a given attribute value. We evaluate our strategy in four different web domains. The results show that the pooling network outperforms previous approaches, which use some kind of input pre-processing, in all domains.
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-to-character RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.
Dynamic Complexity of Parity Games with Bounded Tree-Width
Venue: arXiv cs.CC 2016
Year: 2016

Dynamic complexity is concerned with updating the output of a problem when the input is slightly changed. We study the dynamic complexity of two-player parity games over graphs of bounded tree-width, where updates may add or delete edges, or change the owner or color of states. We show that this problem is in DynFO (with LOGSPACE precomputation); this is achieved by a reduction to a Dyck-path problem on an acyclic automaton.
FogMQ: A Message Broker System for Enabling Distributed, Internet-Scale IoT Applications over Heterogeneous Cloud Platforms
Venue: arXiv cs.NI 2016
Year: 2016

Excessive tail end-to-end latency occurs with conventional message brokers as a result of having massive numbers of geographically distributed devices communicate through a message broker. On the other hand, broker-less messaging systems, though ensure low latency, are highly dependent on the limitation of direct device-to-device (D2D) communication technologies, and cannot scale well as large numbers of resource-limited devices exchange messages. In this paper, we propose FogMQ, a cloud-based message broker system that overcomes the limitations of conventional systems by enabling autonomous discovery, self-deployment, and online migration of message brokers across heterogeneous cloud platforms. For each device, FogMQ provides a high capacity device cloning service that subscribes to device messages. The clones facilitate near-the-edge data analytics in resourceful cloud compute nodes. Clones in FogMQ apply Flock, an algorithm mimicking flocking-like behavior to allow clones to dynamically select and autonomously migrate to different heterogeneous cloud platforms in a distributed manner.
Speech Enhancement via Two-Stage Dual Tree Complex Wavelet Packet Transform with a Speech Presence Probability Estimator
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) estimator and a generalized minimum mean squared error (MMSE) estimator are developed. To overcome the drawback of signal distortions caused by down sampling of WPT, a two-stage analytic decomposition concatenating undecimated WPT (UWPT) and decimated WPT is employed. An SPP estimator in the DTCWPT domain is derived based on a generalized Gamma distribution of speech, and Gaussian noise assumption. The validation results show that the proposed algorithm can obtain enhanced perceptual evaluation of speech quality (PESQ), and segmental signal-to-noise ratio (SegSNR) at low SNR nonstationary noise, compared with other four state-of-the-art speech enhancement algorithms, including optimally modified LSA (OM-LSA), soft masking using a posteriori SNR uncertainty (SMPO), a posteriori SPP based MMSE estimation (MMSE-SPP), and adaptive Bayesian wavelet thresholding (BWT).
Large-scale comparative visualisation of sets of multidimensional data
Venue: arXiv cs.HC 2016
Year: 2016

We present encube $-$ a qualitative, quantitative and comparative visualisation and analysis system, with application to high-resolution, immersive three-dimensional environments and desktop displays. encube extends previous comparative visualisation systems by considering: 1) the integration of comparative visualisation and analysis into a unified system; 2) the documentation of the discovery process; and 3) an approach that enables scientists to continue the research process once back at their desktop. Our solution enables tablets, smartphones or laptops to be used as interaction units for manipulating, organising, and querying data. We highlight the modularity of encube, allowing additional functionalities to be included as required. Additionally, our approach supports a high level of collaboration within the physical environment. We show how our implementation of encube operates in a large-scale, hybrid visualisation and supercomputing environment using the CAVE2 at Monash University, and on a local desktop, making it a versatile solution. We discuss how our approach can help accelerate the discovery rate in a variety of research scenarios.
Parameters Differentiating the Characteristics and Security of Military Information Systems
Venue: arXiv cs.CR 2016
Year: 2016

Revolution in the area of information technology has brought about changes in many spheres of life. Today, information systems are being used in very sensitive areas such as defence and missile control systems, nuclear plants, etc. Not only has it changed how business is conducted, it has also brought about entirely new paradigms like that of information and cyber warfare. Similarly, one of the many impacts that it has made, is how wars are fought. For all what it has contributed, the information stored on digital devices and computers has become a precious resource and special measures are taken to guard it against attacks from malicious users. These special measures are needed by any enterprise be it a business firm, a commercial entity, a government agency or a military organization. However, requirements and specifications for information security and assurance for a military organization are essentially different from those of commercial or business applications. This paper highlights and discusses various aspects related to the security of information resources of military importance and outlines certain parameters that should be taken into consideration when talking about the security of military information resources. W also describe the role satellite reconnaissance can play in cyber war. Authors proclaim that this paper is first such attempt to correlate cyber war with satellite reconnaissance.