bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measuring and Analysis Colour of Birch Wood in Digital Picture
Authors: Hrčková Mária , Koleda Pavol , Koleda Peter 
Published: August 2020

Abstract
The aim of the research was to verify a new method of measuring the colour of wood. The method was designed to automate wood colour measurement. In the experiments birch wood was used, which was heat treated with saturated water steam. Nikon D3200 camera was used in conjunction with the MATLAB program to implement the method. The assessments were conducted in the colour space of CIE L* a* b*. The measured values confirmed the decrease in lightness from natural to thermally modified wood. The trends of the colour values a* and b* were the same as those reported by the authors who conducted experiments with birch wood. Differences were found in the magnitude of the measured chromatic component a* values. It will be necessary to verify the above facts and determine the reasons for these differences by measuring the colour of other wood species.
Lubricant Analysis as the Most Useful Tool in the Proactive Maintenance Philosophies of Machinery and its Components
Authors: Kučera Marián , Kopčanová Silvia , Sejkorová Marie 
Published: August 2020

Abstract
Condition monitoring and fault diagnosis of engineering systems are critical for the stable and reliable operation in various areas as mobile technology (primarily agricultural, forestry, mining and construction machinery), railways, airlines and large fleets. Thus, to achieve a satisfactory level of reliability for the life of a machine, proactive maintenance strategy is the only key. This means that the application of classical reliability methods suitable for components with sudden failures can be complemented by technical diagnostic methods which have the potential to provide the information about the system condition. In this article we focus on the diagnostic signal related to the used oil – tribodiagnostic measures and is an interesting theoretical item related to the evaluation of the quality of lubricants in the aspect of operation. This is because the oil is in direct contact with single parts of the assessed technical systems. Results tests were reviewed and derived from various parameters of lubricants and their limits that highlight the condition and state of the lubricants under varying categories which include, physiochemical, elemental (wear), contamination and additive analysis.
A Linearized Model of FID Signal for Increasing Proton Magnetometer Precision
Authors: Tan Chao , Fu Ruijie , Wu Chenguang , Li Xinglin 
Published: August 2020

Abstract
A linearized model of frequency measurement for the Free Induction Decay (FID) signal is proposed to increase the Proton Magnetometer (PM) precision. First, the nonlinear model of frequency measurement is set up according to the characteristic of the FID signal. Then, according to the error analysis of the MCFM method, the model is linearized on the condition of precision requirement. Furthermore, to reduce the nonlinear error caused by approximate treatment and the trigger time error caused by the random noise, the Least Squares (LS) method is adopted to estimate the slope of the linearized model, and the frequency to be measured is the inverse of the slope. Finally, a PM Prototype is made to verify the effectiveness of the proposed method. Experimental results show that the precision of frequency measurement is obviously increased if the proposed method is adopted for the noised sine signal. Moreover, the RMSD and the NPSD of magnetic-field measurement are about 0.13 nT and 80 pT/Hz1/2, respectively if the proposed method is adopted by PM, which is better than the comparison method.
A Comparison of Non-negative Tucker Decomposition and Parallel Factor Analysis for Identification and Measurement of Human EEG Rhythms
Authors: Rošt’áková Zuzana , Rosipal Roman , Seifpour Saman , Trejo Leonardo Jose 
Published: August 2020

Abstract
Analysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG) plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the brain neural activity can be extracted by a tensor decomposition of continuously recorded multichannel EEG. Parallel factor analysis (PARAFAC) is a powerful approach for this purpose. However, the assumption of the same number of factors in each dimension of the PARAFAC model may be restrictive when applied to EEG data. In this article we discuss the potential benefits of an alternative tensor decomposition method – the Tucker model. We analyze situations, where in comparison to the PARAFAC solution, the Tucker model provides a more parsimonious representation of the EEG data decomposition. We show that this more parsimonious representation of EEG is achieved without reducing the ability to explain variance. We analyze EEG records of two patients after ischemic stroke and we focus on the extraction of specific sensorimotor oscillatory sources associated with motor imagery during neurorehabilitation training. Both models provided consistent results. The advantage of the Tucker model was a compact structure with only two spatial signatures reflecting the expected lateralized activation of the detected subject-specific sensorimotor rhythms.
Analysis of Periodicities in Surface Topography of Cold rolled sheets Using Data Captured by Camera System
Authors: Zavadil Jaromir , Bartunek Josef Strom , Fojtik David 
Published: August 2020

Abstract
A method for surface analysis of cold rolled sheets is proposed in this paper. The approach is based on a low-cost specially built camera system followed by spectral analysis of the data captured from metal surfaces. The focus is on the changes in the surface topography caused by cold rolling with emphasis towards periodicities in the processed surface. Angular profile of the spectrum is calculated and used to display periodicities in surface topography and show their direction. The results obtained by using the proposed system were compared with results obtained from the optical profilometer MicroProf FRT. The experiments show that cold rolling creates marks on the surface of the material, which represent periodicities that can be effectively detected by the proposed method and camera system. Even though the camera system is not able to measure precise surface roughness, it is able to detect periodicities and the results of spectral analysis are comparable with the results from the optical profilometer.
Processing Accuracy of Instantaneous Values of a Stochastic Signal in an Inertial Measurement System
Authors: Kowalczyk Adam , Chorzępa Rafał 
Published: August 2020

Abstract
The article presents an analysis of the dynamic error occurring when processing a stochastic signal in an inertial measurement system. The problem was illustrated using both a calculation and a laboratory example. The technique of conditional averaging of signals was used in the experiment. The possibility to minimize the root mean square value of the error as well as the need for a time correction of measurement values in an inertial measurement system was demonstrated.
Detecting an Unknown Abrupt Change in the Band Center of the Fast-Fluctuating Gaussian Random Process
Authors: Chernoyarov Oleg , Demina Tatiana , Kabanov Yuri , Makarov Alexander 
Published: August 2020

Abstract
The generalized maximum likelihood algorithm is introduced for detecting the abrupt change in the band center of a fast-fluctuating Gaussian random process with the uniform spectral density. This algorithm has a simpler structure than the ones obtained by means of common approaches and it can be effectively implemented on the base of both modern digital signal processors and field-programmable gate arrays. By applying the multiplicative and additive local Markov approximation of the decision statistics and its increments, the analytical expressions are calculated for the false alarm and missing probabilities. And with the help of statistical simulation it is confirmed that the proposed detector is operable, while the theoretical formulas describing its quality and efficiency approximate satisfactorily the corresponding experimental data in a wide range of parameters of the observable data realization.
Time-Frequency Analysis of Partial Discharge Current Pulses in Different Gas Environment under Lightning Impulse
Authors: Arikan Kemal , Önal Emel , Şeker Serhat 
Published: August 2020

Abstract
This paper deals with a time-frequency analysis of the measured partial discharge (PD) currents in different insulation gases. These gas environments consist of a pure SF6 (sulphur-hexafluoride) and sulphur-hexafluoride and nitrogen (1 % SF6 + 99 % N2) mixture, under both positive and negative lightning impulse (LI) voltage stresses. In this study, the short time Fourier transform was used to extract the time-frequency information of PDs for different gases at different pressures, and these results were compared to each other. Thus, the relationship between the time, amplitude, and frequency of PD currents was studied. Moreover, some statistical formulas, such as mean, standard deviation, kurtosis, and skewness were applied to the time-dependent PD current data. As a result, a correlation between obtained statistical results and PD frequencies was examined. In most cases, the frequency of partial discharge decreased when the pressure increased. The amplitude of the partial discharges for negative polarity was more than that for positive polarity gas insulations. The partial discharge amplitudes of the pressure of 2 bar were mostly high compared to other pressures. This case demonstrated that SF6 had a maximum minimum character in terms of breakdown. The partial discharge frequency of a 1 % SF6 mixture was higher than that of pure SF6. It is thought that SF6 suppresses the discharge frequency, and statistical evaluations support the experimental results.
Long-term investigation of 137Cs and 134Cs in drinking water in the city of Zagreb, Croatia
Authors: Franić Zdenko , Branica Gina , Petrinec Branko , Marović Gordana 
Published: August 2020

Abstract
This paper presents the results of long-term investigations of 137Cs and 134Cs activity concentrations in drinking water in the city of Zagreb for the period 1987–2018. The highest activity concentrations of both radio-nuclides were measured in 1987, decreasing exponentially ever since, while 134Cs in several subsequent years fell under the detection limit. After the Fukushima Daiichi accident in 2011, the presence of 134Cs in drinking water was detected again. The environmental residence time for 137Cs was estimated to be 8.1 years in drinking water and 5.7 years in fallout. The correlation between 137Cs in fallout and in drinking water is very good, and this indicates that fallout is the main source of water contamination. The observed 134Cs/137Cs activity ratio in drinking water for the post-Chernobyl period was similar to the ratio found in other environmental samples. The estimation of annual effective doses received by the adult members of the Croatian population due to the intake of radiocaesium in drinking water showed quite small doses of 0.28 μSv in 1987 decreasing to 2.5 nSv in 2018, which indicated that drinking water was not a critical pathway for the transfer of radiocaesium to humans.
Differentially-Private Multi-Party Sketching for Large-Scale Statistics
Authors: Choi Seung Geol , Dachman-soled Dana , Kulkarni Mukul , Yerukhimovich Arkady 
Published: August 2020

AbstractWe consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size.We begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple data sets, including the publicly available IP address data set from University of Michigan’s scans of internet IPv4 space, to determine the trade-offs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes.Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms.
SiegeBreaker: An SDN Based Practical Decoy Routing System
Authors: Sharma Piyush Kumar , Gosain Devashish , Sagar Himanshu , Kumar Chaitanya , Dogra Aneesh , Naik Vinayak , Acharya H.B. , Chakravarty Sambuddho 
Published: August 2020

Abstract

Decoy Routing (DR), a promising approach to censorship circumvention, uses routers (rather than end hosts) as proxy servers. Users of censored networks, who wish to use DR, send specially crafted packets, nominally addressed to an uncensored website. Once safely out of the censored network, the packets encounter a special router (the Decoy Router) which identifies them using a secret handshake, and proxies them to their true destination (a censored site). However, DR has implementation problems: it is infeasible to reprogram routers for the complex operations required. Existing DR solutions fall back on using commodity servers as a Decoy Router. But as servers are not efficient at routing, most web applications show poor performance when accessed over DR. A further concern is that the Decoy Router has to inspect all flows in order to identify the ones that need DR. This may itself be a breach of privacy for other users (who neither require DR nor want to be monitored). In this paper, we present a novel DR system, Siege- Breaker (SB), which solves the aforementioned problems using an SDN-based architecture. Previous proposals involve a single unit which performs all major operations (inspecting all flows, identifying the DR requests and proxying them). In contrast, SB distributes the tasks for DR among three independent modules. (1) The SDN controller identifies DR requests via a covert, privacy preserving scheme, and does not need to inspect all flows. (2) The reconfigurable SDN switch intercepts packets, and forwards them to a secret proxy efficiently. (3) The secret proxy server proxies the client’s traffic to the censored site. Our modular, lightweight design achieves performance comparable to direct TCP downloads, for both in-lab setups, and Internet based tests involving commercial SDN switches.
Comprehensive Anonymity Trilemma: User Coordination is not enough
Authors: Das Debajyoti , Meiser Sebastian , Mohammadi Esfandiar , Kate Aniket 
Published: August 2020

Abstract
For anonymous communication networks (ACNs), Das et al. recently confirmed a long-suspected trilemma result that ACNs cannot achieve strong anonymity, low latency overhead and low bandwidth overhead at the same time. Our paper emanates from the careful observation that their analysis does not include a relevant class of ACNs with what we call user coordination where users proactively work together towards improving their anonymity. We show that such protocols can achieve better anonymity than predicted by the above trilemma result. As the main contribution, we present a stronger impossibility result that includes all ACNs we are aware of. Along with our formal analysis, we provide intuitive interpretations and lessons learned. Finally, we demonstrate qualitatively stricter requirements for the Anytrust assumption (all but one protocol party is compromised) prevalent across ACNs.
The Power of the Hybrid Model for Mean Estimation
Authors: Avent Brendan , Dubey Yatharth , Korolova Aleksandra 
Published: August 2020

Abstract
We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary realvalued distributions with bounded support. When the curator knows the distribution’s variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines.We then analytically characterize how the estimator’s utility is parameterized by the problem setting and parameter choices. When the distribution’s variance is unknown, we design a heuristic hybrid estimator and analyze how it compares to the baselines. We find that it often performs better than the baselines, and sometimes almost as well as the known-variance estimator. We then answer the question of how our estimator’s utility is affected when users’ data are not drawn from the same distribution, but rather from distributions dependent on their trust model preference. Concretely, we examine the implications of the two groups’ distributions diverging and show that in some cases, our estimators maintain fairly high utility. We then demonstrate how our hybrid estimator can be incorporated as a sub-component in more complex, higher-dimensional applications. Finally, we propose a new privacy amplification notion for the hybrid model that emerges due to interaction between the groups, and derive corresponding amplification results for our hybrid estimators.
Effective writing style transfer via combinatorial paraphrasing
Authors: Gröndahl Tommi , Asokan N. 
Published: August 2020

AbstractStylometry can be used to profile or deanonymize authors against their will based on writing style. Style transfer provides a defence. Current techniques typically use either encoder-decoder architectures or rule-based algorithms. Crucially, style transfer must reliably retain original semantic content to be actually deployable. We conduct a multifaceted evaluation of three state-of-the-art encoder-decoder style transfer techniques, and show that all fail at semantic retainment. In particular, they do not produce appropriate paraphrases, but only retain original content in the trivial case of exactly reproducing the text. To mitigate this problem we propose ParChoice: a technique based on thecombinatorial application of multiple paraphrasing algorithms. ParChoice strongly outperforms the encoder-decoder baselines in semantic retainment. Additionally, compared to baselines that achieve nonnegligible semantic retainment, ParChoice has superior style transfer performance. We also apply ParChoice to multi-author style imitation (not considered by prior work), where we achieve up to 75% imitation success among five authors. Furthermore, when compared to two state-of-the-art rule-based style transfer techniques, ParChoice has markedly better semantic retainment. Combining ParChoice with the best performing rulebased baseline (Mutant-X [34]) also reaches the highest style transfer success on the Brennan-Greenstadt and Extended-Brennan-Greenstadt corpora, with much less impact on original meaning than when using the rulebased baseline techniques alone. Finally, we highlight a critical problem that afflictsallcurrent style transfer techniques: the adversary can use the same technique for thwarting style transfer viaadversarial training. We show that adding randomness to style transfer helps to mitigate the effectiveness of adversarial training.
VideoDP: A Flexible Platform for Video Analytics with Differential Privacy
Authors: Wang Han , Xie Shangyu , Hong Yuan 
Published: August 2020

AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.
Running Refraction Networking for Real
Authors: VanderSloot Benjamin , Frolov Sergey , Wampler Jack , Tan Sze Chuen , Simpson Irv , Kallitsis Michalis , Halderman J. Alex , Borisov Nikita , Wustrow Eric 
Published: August 2020

Abstract
Refraction networking is a next-generation censorship circumvention approach that locates proxy functionality in the network itself, at participating ISPs or other network operators. Following years of research and development and a brief pilot, we established the world’s first production deployment of a Refraction Networking system. Our deployment uses a highperformance implementation of the TapDance protocol and is enabled as a transport in the popular circumvention app Psiphon. It uses TapDance stations at four physical uplink locations of a mid-sized ISP, Merit Network, with an aggregate bandwidth of 140 Gbps. By the end of 2019, our system was enabled as a transport option in 559,000 installations of Psiphon, and it served upwards of 33,000 unique users per month. This paper reports on our experience building the deployment and operating it for the first year. We describe how we overcame engineering challenges, present detailed performance metrics, and analyze how our system has responded to dynamic censor behavior. Finally, we review lessons learned from operating this unique artifact and discuss prospects for further scaling Refraction Networking to meet the needs of censored users.
CanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks
Authors: Farooqi Shehroze , Musa Maaz , Shafiq Zubair , Zaffar Fareed 
Published: August 2020

Abstract
Online social networks support a vibrant ecosystem of third-party apps that get access to personal information of a large number of users. Despite several recent high-profile incidents, methods to systematically detect data misuse by third-party apps on online social networks are lacking. We propose CanaryTrap to detect misuse of data shared with third-party apps. CanaryTrap associates a honeytoken to a user account and then monitors its unrecognized use via different channels after sharing it with the third-party app. We design and implement CanaryTrap to investigate misuse of data shared with third-party apps on Facebook. Specifically, we share the email address associated with a Facebook account as a honeytoken by installing a third-party app. We then monitor the received emails and use Facebook’s ad transparency tool to detect any unrecognized use of the shared honeytoken. Our deployment of CanaryTrap to monitor 1,024 Facebook apps has uncovered multiple cases of misuse of data shared with third-party apps on Facebook including ransomware, spam, and targeted advertising.