OpenRaiser/Intern-Atlas huggingface dataset:
Design Guidelines for the User-Centred Collaborative Citizen Science Platforms
Venue: arXiv cs.HC 2016
Year: 2016

Online Citizen Science platforms are good examples of socio-technical systems where technology-enabled interactions occur between scientists and the general public (volunteers). Citizen Science platforms usually host multiple Citizen Science projects, and allow volunteers to choose the ones to participate in. Recent work in the area has demonstrated a positive feedback loop between participation and learning and creativity in Citizen Science projects, which is one of the motivating factors both for scientists and the volunteers. This emphasises the importance of creating successful Citizen Science platforms, which support this feedback process, and enable enhanced learning and creativity to occur through knowledge sharing and diverse participation. In this paper, we discuss how scientists' and volunteers' motivation and participation influence the design of Citizen Science platforms. We present our summary as guidelines for designing these platforms as user-inspired socio-technical systems. We also present the case-studies on popular Citizen Science platforms, including our own CitizenGrid platform, developed as part of the CCL EU project, as well as Zooniverse, World Community Grid, CrowdCrafting and EpiCollect+ to see how closely these platforms follow our proposed guidelines and how these may be further improved to incorporate the creativity enabled by the collective knowledge sharing.
Capacity Planning for Cluster Tools in the Semiconductor Industry
Venue: arXiv cs.DS 2016
Year: 2016

This paper proposes a new model for Cluster-tools with two load locks. Cluster-tools are widely used to automate single wafer processing in semiconductor industry. The load locks are the entry points into the vacuum of the Cluster-tool's mainframe. Usually there are two of them available. Each lot being processed, is dedicated to a single load-lock. Therefore at most two different lots (with possibly different processing times and qualification) can be processed simultaneously. This restriction is one of the major potential bottlenecks. Capacity planning is one of the possible applications for the proposed model and the paper demonstrates the integration into a more general framework that considers different tool types and different operational modes. The paper also generalizes an earlier model that is limited to three processing chambers. The proposed modeling approach is based on makespan reductions by parallel processing. It turns out that the performance of the new approach is similar, when compared to the generalized model for three chambers, but the new approach outperforms the generalized model for four and more chambers.
Efficient Execution of Irregular Wavefront Propagation Pattern on Many Integrated Core Architecture
Venue: arXiv cs.DC 2016
Year: 2016

The efficient execution of image processing algorithms is an active area of Bioinformatics. In image processing, one of the classes of algorithms or computing pattern that works with irregular data structures is the Irregular Wavefront Propagation Pattern (IWPP). In this class, elements propagate information to neighbors in the form of wave propagation. This propagation results in irregular access to data and expansions. Due to this irregularity, current implementations of this class of algorithms requires atomic operations, which is very costly and also restrains implementations with Single Instruction, Multiple Data (SIMD) instructions in Many Integrated Core (MIC) architectures, which are critical to attain high performance on this processor. The objective of this study is to redesign the Irregular Wavefront Propagation Pattern algorithm in order to enable the efficient execution on processors with Many Integrated Core architecture using SIMD instructions. In this work, using the Intel (R) Xeon Phi (TM) coprocessor, we have implemented a vector version of IWPP with up to 5.63x gains on non-vectored version, a parallel version using First In, First Out (FIFO) queue that attained speedup up to 55x as compared to the single core version on the coprocessor, a version using priority queue whose performance was 1.62x better than the fastest version of GPU based implementation available in the literature, and a cooperative version between heterogeneous processors that allow to process images bigger than the Intel (R) Xeon Phi (TM) memory and also provides a way to utilize all the available devices in the computation.
Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications
Venue: arXiv cs.DC 2016
Year: 2016

While the animal bioacoustics community at large is collecting huge amounts of acoustic data at an unprecedented pace, processing these data is problematic. Currently in bioacoustics, there is no effective way to achieve high performance computing using commericial off the shelf (COTS) or government off the shelf (GOTS) tools. Although several advances have been made in the open source and commercial software community, these offerings either support specific applications that do not integrate well with data formats in bioacoustics or they are too general. Furthermore, complex algorithms that use deep learning strategies require special considerations, such as very large libraiers of exemplars (whale sounds) readily available for algorithm training and testing. Detection-classification for passive acoustics is a data-mining strategy and our goals are aligned with best practices that appeal to the general data mining and machine learning communities where the problem of processing large data is common. Therefore, the objective of this work is to advance the state-of-the art for data-mining large passive acoustic datasets as they pertain to bioacoustics. With this basic deficiency recognized at the forefront, portions of the grant were dedicated to fostering deep-learning by way of international competitions (kaggle.com) meant to attract deep-learning solutions. The focus of this early work was targeted to make significant progress in addressing big data systems and advanced algorithms over the duration of the grant from 2012 to 2015. This early work provided simulataneous advances in systems-algorithms research while supporting various collaborations and projects.
A practical attack to Bouftass's cryptosystem
Venue: arXiv cs.CR 2016
Year: 2016

Recently, a new fast public key exchange protocol was presented by S. Bouftass. The protocol is based on the difficulty of inverting the function $F(x)=\lfloor (zx \mod 2^p)/ 2^q \rfloor$. In this paper, we describe a practical attack against this protocol based on Closest Vector Problem (CVP) and Gaussian lattice reduction.
Blackbox: A procedure for parallel optimization of expensive black-box functions
Venue: arXiv cs.MS 2016
Year: 2016

This note provides a description of a procedure that is designed to efficiently optimize expensive black-box functions. It uses the response surface methodology by incorporating radial basis functions as the response model. A simple method based on a Latin hypercube is used for initial sampling. A modified version of CORS algorithm with space rescaling is used for the subsequent sampling. The procedure is able to scale on multicore processors by performing multiple function evaluations in parallel. The source code of the procedure is written in Python.
A Novel Approach for Imputation of Missing Attribute Values for Efficient Mining of Medical Datasets - Class Based Cluster Approach
Venue: arXiv cs.IR 2016
Year: 2016

Missing attribute values are quite common in the datasets available in the literature. Missing values are also possible because all attributes values may not be recorded and hence unavailable due to several practical reasons. For all these one must fix missing attribute vales if the analysis has to be done. Imputation is the first step in analyzing medical datasets. Hence this has achieved significant contribution from several medical domain researchers. Several data mining researchers have proposed various methods and approaches to impute missing values. However very few of them concentrate on dimensionality reduction. In this paper, we discuss a novel imputation framework for missing values imputation. Our approach of filling missing values is rooted on class based clustering approach and essentially aims at medical records dimensionality reduction. We use these dimensionality records for carrying prediction and classification analysis. A case study is discussed which shows how imputation is performed using proposed method.
Chill-Pass: Using Neuro-Physiological Responses to Chill Music to Defeat Coercion Attacks
Venue: arXiv cs.CR 2016
Year: 2016

Current alphanumeric and biometric authentication systems cannot withstand situations where a user is coerced into releasing their authentication materials under hostile circumstances. Existing approaches of coercion resistant authentication systems (CRAS) propose authentication factors such as implicit learning tasks, which are non-transferable, but still have the drawback that an attacker can force the victim (causing stress) to perform the task in order to gain unauthorized access. Alternatively, there could be cases where the user could claim that they were coerced into giving up the authentication materials, whereas in reality they acted as an insider attacker. Therefore, being able to detect stress during authentication also helps to achieve non-repudiation in such cases. To address these concerns, we need CRAS that have both the non-transferable property as well as a mechanism to detect stress related to coercion. In this paper, we study the feasibility of using Chill (intensely pleasurable) music as a stimulus to elicit unique neuro-physiological responses that can be used as an authenticating factor for CRAS. Chill music and stress are both stimuli for a neuro-chemical called Dopamine. However, they release the Dopamine at different parts of the brain, resulting in different neuro-physiological responses, which gives us both the non-transferable and stress-detection properties necessary for CRAS. We have experimentally validated our proposed Chill music based CRAS using human subjects and measuring their neuro-physiological responses on our prototype system. Based on the 100 samples collected from the subjects, we were able to successfully authenticate the subjects with an accuracy of over 90\%. Our work not only demonstrates the potential of Chill music as a unique stimulus for CRAS, but also paves the path of wider adoption of CRAS in general.
On the Co-existence of TD-LTE and Radar over 3.5 GHz Band: An Experimental Study
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a pioneering study based on a series of experiments on the operation of commercial Time-Division Long-Term Evolution (TD-LTE) systems in the presence of pulsed interfering signals in the 3550-3650 MHz band. TD-LTE operations were carried out in channels overlapping and adjacent to the high power SPN-43 radar with various frequency offsets between the two systems to evaluate the susceptibility of LTE to a high power interfering signal. Our results demonstrate that LTE communication using low antenna heights was not adversely affected by the pulsed interfering signal operating on adjacent frequencies irrespective of the distance of interfering transmitter. Performance was degraded only for very close distances (1-2 km) of overlapping frequencies of interfering transmitter.
Reliability Testing Strategy - Reliability in Software Engineering
Venue: arXiv cs.SE 2016
Year: 2016

This paper presents the core principles of reliability in software engineering - outlining why reliability testing is critical and specifying the process of measuring reliability. The paper provides insight for both novice and experts in the software engineering field for assessing failure intensity as well as predicting failure of software systems. Measurements are conducted by utilizing information from an operational profile to further enhance a test plan and test cases, all of which this paper demonstrates how to implement.
From Network Sharing to Multi-tenancy: The 5G Network Slice Broker
Venue: arXiv cs.NI 2016
Year: 2016

The ever-increasing traffic demand is pushing network operators to find new cost-efficient solutions towards the deployment of future 5G mobile networks. The network sharing paradigm was explored in the past and partially deployed. Nowadays, advanced mobile network multi-tenancy approaches are increasingly gaining momentum paving the way towards further decreasing Capital Expenditures and Operational Expenditures (CAPEX/OPEX) costs, while enabling new business opportunities. This paper provides an overview of the 3GPP standard evolution from network sharing principles, mechanisms and architectures to future on-demand multi-tenant systems. In particular, it introduces the concept of the 5G Network Slice Broker in 5G systems, which enables mobile virtual network operators, over-the-top providers and industry vertical market players to request and lease resources from infrastructure providers dynamically via signaling means. Finally, it reviews the latest standardization efforts considering remaining open issues for enabling advanced network slicing solutions taking into account the allocation of virtualized network functions based on ETSI NFV, the introduction of shared network functions and flexible service chaining.
Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity
Venue: arXiv cs.DB 2016
Year: 2016

We give solutions to two fundamental computational problems in ontology-based data access with the W3C standard ontology language OWL 2 QL: the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs), and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering, and whether all OMQs in the class have polynomial-size first-order, positive existential, and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.
An Upper Bound for the Capacity of Amplitude-Constrained Scalar AWGN Channel
Venue: arXiv cs.IT 2016
Year: 2016

This paper slightly improves the upper bound in Thangaraj et al. for the capacity of the amplitude-constrained scalar AWGN channel. This improvement makes the upper bound within 0.002 bits of the capacity for $\frac{E_b}{N_0}\leq 2.5$ dB.
Learning Covariant Feature Detectors
Venue: arXiv cs.CV 2016
Year: 2016

Local covariant feature detection, namely the problem of extracting viewpoint invariant features from images, has so far largely resisted the application of machine learning techniques. In this paper, we propose the first fully general formulation for learning local covariant feature detectors. We propose to cast detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. We then derive a covariance constraint that can be used to automatically learn which visual structures provide stable anchors for local feature detection. We support these ideas theoretically, proposing a novel analysis of local features in term of geometric transformations, and we show that all common and many uncommon detectors can be derived in this framework. Finally, we present empirical results on translation and rotation covariant detectors on standard feature benchmarks, showing the power and flexibility of the framework.
Characterizing Solution Concepts in Terms of Common Knowledge of Rationality
Venue: arXiv cs.GT 2016
Year: 2016

Characterizations of Nash equilibrium, correlated equilibrium, and rationalizability in terms of common knowledge of rationality are well known. Analogous characterizations of sequential equilibrium, (trembling hand) perfect equilibrium, and quasi-perfect equilibrium using results of Halpern [2009].
Tools for assessing and optimizing the energy requirements of high performance scientific computing software
Venue: arXiv cs.SE 2016
Year: 2016

Score-P is a measurement infrastructure originally designed for the analysis and optimization of the performance of HPC codes. Recent extensions of Score-P and its associated tools now also allow the investigation of energy-related properties and support the user in the implementation of corresponding improvements. Since it would be counterproductive to completely ignore performance issues in this connection, the focus should not be laid exclusively on energy. We therefore aim to optimize software with respect to an objective function that takes into account energy and run time.
Compression and the origins of Zipf's law for word frequencies
Venue: arXiv cs.CL 2016
Year: 2016

Here we sketch a new derivation of Zipf's law for word frequencies based on optimal coding. The structure of the derivation is reminiscent of Mandelbrot's random typing model but it has multiple advantages over random typing: (1) it starts from realistic cognitive pressures (2) it does not require fine tuning of parameters and (3) it sheds light on the origins of other statistical laws of language and thus can lead to a compact theory of linguistic laws. Our findings suggest that the recurrence of Zipf's law in human languages could originate from pressure for easy and fast communication.
Single Channel Speech Enhancement Using Outlier Detection
Venue: arXiv cs.SD 2016
Year: 2016

Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.
Accelerating Deep Learning with Shrinkage and Recall
Venue: arXiv cs.LG 2016
Year: 2016

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
Venue: arXiv cs.CV 2016
Year: 2016

In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.
Combating Malicious DNS Tunnel
Venue: arXiv cs.CR 2016
Year: 2016

This paper proposes a defense scheme against malicious use of DNS tunnel. A tunnel validator is designed to provide trustworthy tunnel-aware defensive recursive service. In addition to the detection algorithm of malicious tunnel domains, the tunnel validation relies on registered tunnel domains as whitelist and identified malicious tunnel domains as blacklist. A benign tunnel user is thus motivated to register its tunnel domain before using it. Through the tunnel validation, the secure domains are allowed to the recursive service provided by the tunnel validator and the insecure domains are blocked. All inbound suspicious DNS queries are recorded and stored for forensics and future malicious tunnel detection by the tunnel validator.
Challenging Fuel Cycle Modeling Assumptions: Facility and Time Step Discretization Effects
Venue: arXiv cs.CE 2016
Year: 2016

Due to the diversity of fuel cycle simulator modeling assumptions, direct comparison and benchmarking can be difficult. In 2012 the Organisation for Economic Co-operation and Development completed a benchmark study that is perhaps the most complete published comparison performed. Despite this, various results from the simulators were often significantly different because of inconsistencies in modeling decisions involving reprocessing strategies, refueling behavior, reactor end-of-life handling, etc. This work identifies and quantifies the effects of selected modeling choices that may sometimes be taken for granted in the fuel cycle simulation domain. Four scenarios are compared using combinations of fleet-based or individually modeled reactors with monthly or quarterly (3-month) time steps. The scenarios approximate a transition from the current U.S. once-through light water reactor fleet to a full sodium fast reactor fuel cycle. The Cyclus fuel cycle simulator's plug-in capability along with its market-like dynamic material routing allow it to be used as a level playing field for comparing the scenarios. When under supply-constraint pressure, the four cases exhibit noticeably different behavior. Fleet-based modeling is more efficient in supply-constrained environments at the expense of losing insight on issues such as realistically suboptimal fuel distribution and challenges in reactor refueling cycle staggering. Finer-grained time steps enable more efficient material use in supply-constrained environments resulting in lower standing inventories of separated Pu. Large simulations with fleet-based reactors run much more quickly than their individual reactor counterparts. Gaining a better understanding of how these and other modeling choices affect fuel cycle dynamics will enable making more deliberate decisions with respect to trade-offs such as computational investment vs. realism.