nils-herrmann/clean_scirepeval huggingface dataset:
On automatically detecting similar Android apps
Venue: 2016 IEEE 24th International Conference on Program Comprehension (ICPC)

Detecting similar applications is a challenging problem, since it implies that similar high level features and their low level implementations can be detected and matched automatically. We propose an approach for automatically detecting Closely reLated applications in ANdroid (CLANdroid) by relying on advanced Information Retrieval techniques and five semantic anchors: identifiers, Android APIs, intents, permissions, and sensors. To evaluate CLANdroid we created a benchmark consisting of 14,450 apps along with information on similar apps provided by Google Play. We also compared effectiveness of different semantic anchors for detecting similar apps as perceived by 27 users. The results show that using Android specific semantic anchors are useful for detecting similar Android apps across different categories. We also measured the impact of third party libraries and obfuscated code when identifying similar Android apps, and our results suggest that there is significant difference in the accuracy when third party libraries are excluded.
SecuRank: Starving Permission Hungry Apps Using Contextual Permission Analysis
Venue: SPSM@CCS

Competition among app developers has caused app stores to be permeated with many groups of general purpose apps that are functionally similar. Examples are the many flashlight or alarm clock apps to choose from. Within groups of functionally similar apps, however, permission usage by individual apps sometimes varies widely. Although (run time) permission warnings inform users of the sensitive access required by apps, many users continue to ignore these warnings due to conditioning or a lack of understanding. Thus, users may inadvertently expose themselves to additional privacy and security risks by installing a more permission hungry app when there was a functionally similar alternative that used less permissions. We study the variation in permission usage across 50,000 Google Play Store search results for 2500 searches each yielding a group of 20 functionally similar apps. Using fine grained contextual analysis of permission usage within groups of apps, we identified over 3400 (potentially) over privileged apps, approximately 7% of the studied dataset. We implement our contextual permission analysis framework as a tool, called SecuRank, and release it to the general public in the form of an Android app and website. SecuRank allows users to audit their list of installed apps to determine whether any of them can be replaced with a functionally similar alternative that requires less sensitive access to their device. By running SecuRank on the entire Google Play Store, we discovered that up to 50% of apps can be replaced with preferable alternative, with free apps and very popular apps more likely to have such alternatives.
Multi view Deep Network for Cross View Classification
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Cross view recognition that intends to classify samples between different views is an important problem in computer vision. The large discrepancy between different even heterogenous views make this problem quite challenging. To eliminate the complex (maybe even highly nonlinear) view discrepancy for favorable cross view recognition, we propose a multi view deep network (MvDN) which seeks for a non linear discriminant and view invariant representation shared between multiple views. Specifically, our proposed MvDN network consists of two sub networks, view specific sub network attempting to remove view specific variations and the following common sub network attempting to obtain common representation shared by all views. As the objective of MvDN network, the Fisher loss, i.e. the Rayleigh quotient objective, is calculated from the samples of all views so as to guide the learning of the whole network. As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative. The experiments of face recognition across pose and face recognition across feature type on three datasets with 13 and 2 views respectively demonstrate the superiority of the proposed method, especially compared to the typical linear ones.
Demographic indicators of change in a deposit feeding abyssal holothurian community (Station M, 4000 m)

Holothurians are among the most abundant benthic megafauna at abyssal depths, and important consumers and bioturbators of organic carbon on the sea floor. Significant fluctuations in abyssal holothurian density are often attributed to species specific responses to variable particulate organic carbon flux (food supply) stemming from surface ocean events. We report changes in densities of 19 holothurian species at the abyssal monitoring site Station M in the northeast Pacific, recorded during 11 remotely operated vehicle surveys between Dec 2006 and Oct 2014. Body size demographics are presented for Abyssocucumis abyssorum, Synallactidae sp. 1, Paelopatides confundens Elpidia sp. A, Peniagone gracilis Peniagone papillata Peniagone vitrea Peniagone sp. A, Peniagone sp. 1, and Scotoplanes globosa Densities were lower and species evenness was higher from 2006 2009 compared to 2011 2014. Food supply of freshly settled phytodetritus was exceptionally high during this latter period. Based on relationships between median body length and density, numerous immigration and juvenile recruitment events of multiple species appeared to take place between 2011 and 2014. These patterns were dominated by elpidiids (Holothuroidea: Elasipodida: Elpidiidae) which consistently increased in density during a period of high food availability, while other groups showed inconsistent responses. We considered minimum body length to be a proxy for size at juvenile recruitment. Patterns in density clustered by this measure, which was a stronger predictor of maximum density than median and mean body length.
What is DevOps? A Systematic Mapping Study on Definitions and Practices
Venue: XP Workshops

DevOps, the combination of Development and Operations, is a new way of thinking in the software engineering domain that recently received much attention. Given that DevOps is a new term and novel concept recently introduced, no common understanding of what it entails has been achieved yet. Consequently, definitions of DevOps often only represent a part that is relevant to the concept. This study aims to characterize DevOps by exploring central components of DevOps definitions reported in the literature, specifying practices explicitly proposed for DevOps and investigating the similarities and differences between DevOps and other existing methods in software engineering.  A systematic mapping study was conducted that used six electronic databases:   44 studies have been selected that report a definition of DevOps, 15 studies explicitly stating DevOps practices, and 15 studies stating how DevOps is related to other existing methods. Papers in some cases stated a combination of a definition, practices, and relations to other methods, the total number of primary studies was 49.  We proposed a definition for DevOps which may overcome inconsistencies over the various existing definitions of individual research studies. In addition, the practices explicitly proposed for DevOps have been presented as well as the relation to other software development methods.
yanggeugseongjangae hwanjayi K WAIS IV peuropail bunseog

The current study was conducted to compare the level of cognitive functioning in patients with bipolar disorder by utilizing the brand new version of the Wechsler intelligence scale, K WAIS IV, and to understand their cognitive characteristics by comparing the bipolar patients group and the normal control group(NC) In addition, the purpose of this study was to understand the relation between cognitive functioning and clinical variables including the age of onset, the length of disorder, the number of mood episodes, the number of previous hospitalizations, and the severity of mood symptoms. K WAIS IV was performed for 58 bipolar patients and 300 people from NC for comparison of their Full Scale IQ(FSIQ) 4 index scaled scores, and 10 scaled scores of subtests. Bipolar patients showed significantly lower performance on FSIQ, working memory index(WMI) and processing speed index(PSI) when compared to NC, and, in particular, they showed the lowest performances on PSI. The performances in digit span, arithmetic, symbol search, and coding, the subtests of WMI and PSI were also significantly poor. These results imply cognitive inefficiency in patients with bipolar disorder. However, no significant correlations were observed among the clinical variables and the FSIQ, four indices.
A 0.7 V, 40 nW Compact, Current Mode Neural Spike Detector in 65 nm CMOS
Venue: IEEE Transactions on Biomedical Circuits and Systems

In this paper, we describe a novel low power, compact, current mode spike detector circuit for real time neural recording systems where neural spikes or action potentials (AP) are of interest. Such a circuit can enable massive compression of data facilitating wireless transmission. This design can generate a high signal to noise ratio (SNR) output by approximating the popularly used nonlinear energy operator (NEO) through standard analog blocks. We show that a low pass filter after the NEO can be used for two functions (i) estimate and cancel low frequency interference and (ii) estimate threshold for spike detection. The circuit is implemented in a 65 nm CMOS process and occupies 200 mm x 150 mm of chip area. Operating from a 0.7 V power supply, it consumes about 30 nW of static power and 7 nW of dynamic power for 100 Hz input spike rate making it the lowest power consuming spike detector reported so far.
Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features
Venue: IEEE Signal Processing Letters

Visual object tracking is a challenging task due to object appearance changes caused by shape deformation, heavy occlusion, background clutters, illumination variation, and camera motion. In this letter, we propose a novel robust algorithm which decomposes the task of tracking into translation and scale estimation. We estimate the translation by using five correlation filters with hierarchical convolutional features which produced multilevel correlation response maps to collaboratively infer the target location. We also calculate the scale variation by another correlation filter with histogram of oriented gradient features at the same time. Extensive experimental results on a large scale 50 challenging benchmark dataset show that the proposed algorithm achieved outstanding performance against state of the art methods.
An evaluation of time series smoothing algorithms for land cover classifications using MODIS NDVI multi temporal data

In this study we compared the Savitzky Golay, asymmetric Gaussian, double logistic, Whittaker smoother, and discrete Fourier transformation smoothing algorithms (noise reduction) applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data, to provide continuous phenology data used for land cover (LC) classifications across the Laurentian Great Lakes Basin (GLB) MODIS 16 day 250 m NDVI imagery for the GLB was used in conjunction with National Land Cover Database (NLCD) from 2001, 2006 and 2011, and the Cropland Data Layers (CDL) from 2011 to 2014 to conduct classification evaluations. Inter class separability was measured by Jeffries Matusita (JM) distances between selected cover type pairs (both general classes and specific crops) and intra class variability was measured by calculating simple Euclidean distance for samples within cover types. For the GLB, we found that the application of a smoothing algorithm significantly reduced image noise compared to the raw data. However, the Jeffries Matusita (JM) measures for smoothed NDVI temporal profiles resulted in large inconsistencies. Of the five algorithms tested, only the Fourier transformation algorithm and Whittaker smoother improved inter class separability for corn soybean class pair and significantly improved overall classification accuracy. When compared to the raw NDVI data as input, the overall classification accuracy from the Fourier transformation and Whittaker smoother improved performance by approximately 2 6% for some years. Conversely, the asymmetric Gaussian and double logistic smoothing algorithms actually led to degradation of classification performance.
Detecting Laughter and Filler Events by Time Series Smoothing with Genetic Algorithms
Venue: SPECOM

Social signal detection, where the aim is to identify vocalizations like laughter and filler events (sounds like "eh" "er" etc. is a popular task in the area of computational paralinguistics, a subfield of speech technology. Recent studies have shown that besides applying state of the art machine learning methods, it is worth making use of the contextual information and adjusting the frame level scores based on the local neighbourhood. In this study we apply a weighted average time series smoothing filter for laughter and filler event identification, and set the weights using genetic algorithms. Our results indicate that this is a viable way of improving the Area Under the Curve (AUC) scores: our resulting scores are much better than the accuracy of the raw likelihoods produced by both AdaBoost.MH and DNN, and we also significantly outperform standard time series filters as well.
A novel feature subspace selection method in random forests for high dimensional data
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

Random forests are a class of ensemble methods for classification and regression with randomizing mechanism in bagging instances and selecting feature subspace. For high dimensional data, the performance of random forests degenerates because of the random sampling feature subspace for each node in the construction of decision trees. To address the issue, in this paper, we propose a new Principal Component Analysis and Stratified Sampling based method, called PCA SS, for feature subspace selection in random forests with high dimensional data. For each decision tree in the forests, we firstly create the training data by bagging instances and partition the feature set into several feature subsets. Principal Component Analysis (PCA) is applied on each feature subset to obtain transformed features. All the principal components are retained in order to preserve the variability information of the data. Secondly, depending on a certain principal components principle, the transformed features are partitioned into informative and less informative parts. When constructing each node of decision trees, a feature subspace is selected by stratified sampling method from the two parts. The PCA SS based Random Forests algorithm, named PSRF, ensures enough informative features for each tree node, and it also increases the diversity between the trees to a certain extent. Experimental results demonstrate that the proposed PSRF significantly improves the performance of random forests when dealing with high dimensional data, compared with the state of the art random forests algorithms.
Improving problem reduction for 0 1 Multidimensional Knapsack Problems with valid inequalities
Venue: Comput. Oper. Res.

This paper investigates the problem reduction heuristic for the Multidimensional Knapsack Problem (MKP) The MKP formulation is first strengthened by the Global Lifted Cover Inequalities (GLCI) using the cutting plane approach. The dynamic core problem heuristic is then applied to find good solutions. The GLCI is described in the general lifting framework and several variants are introduced. A Two level Core problem Heuristic is also proposed to tackle large instances. Computational experiments were carried out on classic benchmark problems to demonstrate the effectiveness of this new method. HighlightsPropose a dynamic core problem heuristic strengthened by valid inequalities for MKP.Describe the Global Lifted Cover Inequalities (GLCI) in the general lifting framework.Design a Two level Core problem Heuristic to tackle large problems.
Mildly exponential reduction from gap 3SAT to polynomial gap label cover
Venue: Electron. Colloquium Comput. Complex.

We show that if gap 3SAT has no sub exponential time algorithms then a weak form of the sliding scale conjecture holds. Namely, for every a 0 any algorithm for na approximating the value of label cover must run in time at least nO(exp(1/a) where n is the size of the instance. Put differently, if there is a polynomial time algorithm for approximating the value of label cover to within a factor of approximation of no(1) then gap 3SAT (and thus, random 3SAT) must have faster than exponential time algorithms. Our proof is a twist on the well studied parallel repetition reduction from 3SAT to label cover. Our key observation is that if we take unordered repetition, replacing tuples by sets, then we can afford to take the number of repetitions to be linear in n, and generate an instance of size exp(n) which results in the claimed parameters.
A new fast reduction algorithm for binary knapsack problem
Venue: 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)

The first reduction algorithm for binary knapsack problems was developed by Ingargiola and Korsh (1973) It is a preprocessing algorithm which significantly reduces the number of variables by fixing the optimal value of as many variables as possible. However, the speed of the algorithm is pretty slow, because it only fixes one variable at one time. This paper presents a new reduction algorithm for binary knapsack problem which can fix several variables at one time. When combined with available algorithms for binary knapsack problems, it can substantially reduce the total running time and the space requirements.
Thermal energy storage (TES) for industrial waste heat (IWH) recovery: A review

Industrial activities have a huge potential for waste heat recovery. In spite of its high potential, industrial waste heat (IWH) is currently underutilized. This may be due, on one hand, to the technical and economic difficulties in applying conventional heat recovery methods and, on the other, the temporary or geographical mismatch between the energy released and its heat demand. Thermal energy storage (TES) is a technology which can solve the existing mismatch by recovering the IWH and storing it for a later use. Moreover, the use of recovered IWH leads to a decrease of CO2 emissions and to economic and energy savings. Depending on the distance between the IWH source and the heat demand, TES systems can be placed on site or the IWH can be transported by means of mobile TES systems, to an off site heat demand. Around 50 industry case studies, in which both on site and off site recovery systems are considered are here reviewed and discussed taking into account the characteristics of the heat source, the heat, the TES system, and the economic, environmental and energy savings. Besides, the trends and the maturity of the cases reviewed have been considered. On site TES systems in the basic metals manufacturing are the technology and industrial sector which has focused the most attention among the researchers, respectively. Moreover, water (or steam) erythritol and zeolite are the TES materials used in most industries and space comfort and electricity generation are the most recurrent applications.
The New York Head A precise standardized volume conductor model for EEG source localization and tES targeting
Venue: NeuroImage

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES) a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semiautomated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI) and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27) Additionally, existing reference models for the human head often do not include the cerebrospinal fluid (CSF) and their field of view excludes portions of the head and neck two factors that demonstrably affect current flow patterns. Here we present a highly detailed FEM, which we call ICBM NY, or "New York Head" It is based on the ICBM152 anatomical template (a non linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5 mm 3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth' is compared to its BEM counterpart, the ICBM NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption.
A review on the air PCM TES application for free cooling and heating in the buildings

Thermal energy storage (TES) technologies incorporating phase change materials (PCM) are proving viable option for achieving energy efficiency economically in the buildings. This paper reviews the application of air PCM TES studies and technologies for the free cooling and heating of buildings. TES in general and air PCM TES in particular are discussed in this review. An extensive investigation on air PCM TES systems has been undertaken through passive and active methods and the advantages and disadvantages for each, are detailed. The thermal performances of these systems have been investigated through experimental and numerical approaches and listed in this paper. Passive methods e.g. use of PCMs in the building envelope present difficulty of exchanging a high rate of heat and therefore unsuitable for extreme climate. Therefore, active methods are adopted for extreme climates to meet the demand. Overall, when the right method is applied, air PCM TES systems have shown to be effectively providing free cooling and heating of the buildings through auxiliary sources.