Reviewerly/exHarmony huggingface dataset:
sElect

Online voting is a challenging socio-technical problem that is still an open research question. Current approaches are based on involved cryptographic solutions that hardly can be explained to the average voter. Still, attackers may be able to circumvent the system without breaking the cryptographic protocols. In this paper, we demonstrate a toy voting protocol based on additive homomorphic encryption and two non-colluding parties. The protocol design aims at preserving the essential security properties such as election integrity and voter anonymity while being much simpler to explain. We propose a RESTful implementation of a web front-end of the proposed system. We highlight the fact that cryptography is only one facet of security. By conducting several web attack scenarios to test the robustness of the web interface, we show that the system may still be compromised without breaking the cryptography.
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are based on Gaussian transition model, thus often have difficulty in describing a more complex transition model, e.g. aircraft motions. To resolve the challenge, this paper proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics. Furthermore, we extend the model to the information-theoretic framework, the so-called InfoSSM, by introducing a mutual information regularizer helping the model to learn interpretable and distinguishable multiple dynamics models. Two illustrative numerical experiments in simple Dubins vehicle and high-fidelity flight simulator are presented to demonstrate the performance and interpretability of the proposed model. Finally, this paper introduces a framework using InfoSSM with Bayesian filtering for air traffic control tracking.
An Investigation of Digital Image Correlation for Earth Materials

Strain measurement is vital in material and mechanical testing. The conventional contact based approaches as strain gauges, extensometers, acoustic emission, seismic waves etc. have limitations like sensitivity to contact areas, susceptible to external noise, vulnerable to breakage, influenced by surrounding environment etc. Recently, development of noncontact based methods has eliminated these challenges. Digital Image Correlation is an optical, noncontact based technique which is being widely used in measuring full-field displacement. The technique is based on acquisition of digital images taken before and during loading and developing a correlation between them with subpixel accuracy. In this paper, the behavior of equivalent coal specimens at varying depth from 92 to 126 m have been evaluated for static loading condition using non-contact based approach i.e. Reliability-guided Digital Image Correlation technique (RG-DIC). There exists a good agreement between the contact (strain gauge) and noncontact based approaches (DIC) for static loading condition. So, Digital Image Correlation (DIC) can also be a reliable strain measuring approach for investigating for earth materials.
Openness of the Expert Musician to Innovations to Introduce Civilizations Specifics of the Musical Excellence

The aim of the research is to highlight both the mechanism of thinking and general psychological principles of creativity as part of the musicians` activity initiative. The scientific novelty of the research lies in the fact that for the first time in art studies, through the comparison of observations of experts and understanding of apperception (the process in which the elements of consciousness become clear), on which the Renaissance scientists formulated the term Dialogics as the law of the author’s discovery, the concept of creative roll is declared as an active prolongation of psychological pattern for the expert to be ready for innovations to combine the experience of a professional tradition with action motivation quoted by society. Methodology of the research has been analytical, comparative-historical and psychological-orientational approaches, which allowed by comparison establishing the similarity and the difference between historical phenomena in ensuring the interaction of scientific-creative and artistic and poetic decisions in the activities of the expert musician as essentially similar fields of activity and further development of this concept. In addition, the method of art style comparative art as a mechanism of artistic thinking is used, which prompts an active search to create new samples of creative and pedagogical activity and to choose the most effective methods among them. Conclusions. It is proved that the mechanism of artistic thinking inspires the expert musician and a teacher for active search, to create new samples of creative and pedagogical activity and to choose among them the most effective ones. However, its implementation in the creative product depends not only on individual intentions, but also on the external-social circumstances that determine the extent and direction of the implementation of individual needs in the independent targeting of the expert musician. It is the latter, together with the accumulation of experience, in accordance with the law of the Renaissance Dialogics, that designed to introduce the expert into an active search, where the innovative specificity of creative discovery is a result. Thus, the musician’s readiness to innovate in professional activity is defined as a stable new formation in the structure of the musician personality, which manifests itself in the direction of his actions for the continuous improvement, optimization and restoration of professional activity, realized in teaching, development and education both personal and for students.
Effects of Radar Interference on Target Tracking and Sensor Fusion on Automotive Applications

Demanding intelligent applications in automotive safety and autonomy domain leads to integration of many different types and number of radar sensors in the vehicles. Due to this factor, interference effects among the different radars from ego and other traffic participants becomes an unavoidable issue. Among other destructive effects, radar interference can cause range reduction. This lead to impact on applications such as Extended Object Tracking operating on the radar detection level data and this problem has to be investigated. In this investigation, missing radar detection data and the detection range reduction caused by radar interference effects are simulated with a Point Processes obtained from real world radar data. This paper introduces method for radar interference simulation on the detection level and examines the impact of simulated radar interference on radar Extended Object Tracking algorithm.
Regression Spline-Model in Machine Learning for Signal Prediction and Parameterization

Traditionally, polynomial models are used to analyze and simulate time series. The parameters of these models are estimated using the ordinary least squares (OLS) method. The complex form of the time series requires the complication of the polynomial and the growth of its order. This leads to problems with the conditionality of the equations and the inadequacy of the model. Applying splines allows you to partially solve these problems. In this paper it is proposed to use cubic Hermite splines with infinite first and last fragments to analyze and predict time series, which allows using spline to predict. To estimate the spline parameters, the least squares method is used. For placement of nodes (docking points) algorithms of coordinate optimization with constraints and algorithm of sequential buildup of fragments are offered. In order to avoid over-training or under-training of the spline model, randomness control of residues is proposed. The results are applied for parameterization of heart sounds.
Hamming polynomial of a demimatroid

Johnsen, Roksvold and Verdure have shown how fundamental invariants and concepts of linear coding theory, such as weight enumerators and higher weights, can be studied in the context of matroid theory. They have shown that Betti numbers, of a matroid and its elongations, associated to a linear code, determine both the weight enumerator and the higher weights of the matroid. In the same vein, Britz, Johnsen, Mayhew and Shiromoto, introduced demimatroids as a natural generalization of matroids. They have shown that demimatroids are the appropriate combinatorial objects for studying Wei's duality. Continuing in this line of research, we study the Hamming polynomial of a demimatroid, which is a generalization of the extended weight enumerator of a matroid. It results that the Hamming polynomial is a specialization of the Tutte polynomial of the demimatroid, and actually is equivalent to it. In addition, guided by the work of Johnsen, Roksvold and Verdure for matroids, we prove that Betti numbers of, a demimatroid and its elongations, determine the Hamming polynomial. Our results may be applied to simplicial complexes since in a canonical way they can be viewed as demimatroids. Furthermore, following work of Brylawski and Gordon, we show how demimatroids may be generalized one step further, to Brylawski structures. All this largely extends notions such as deletion, contraction, duality, nullity, minors, weight enumerators, higher weights, Tutte polynomial and MacWilliams identity, to non-matroidal structures.
Error analysis in teaching mathematical informatics

The article describes methods and preliminary results of error analysis based on educational research in teaching mathematical informatics. We present our error typology through hierarchical evaluation of students test. The method is based on diagnostic error vector with variable evaluation criteria. It provides weak and strong evaluation of students’ performance.
Hybrid recommendation algorithm based on multi-attribute rating from online reviews

As the growing users and products in network shopping platform under the big data environment, the data sparsity and cold start problem are increasingly prominent which lead to the recommended effect of recommendation algorithm can't be satisfied by users. For this problem, the paper presents a construction method of user preference model and product feature model based on information mining of online reviews, and then it eases data sparsity through multi-attribute rating. And the paper solves the problem of user cold start and product cold start to a certain extent through the algorithm of similarity which is based user attributes and product attributes. Finally, the paper combines with multiple similarity algorithms to construct hybrid recommendation algorithm based on user preference and product feature. Simulation experiments verify the ability to solve the cold start problem and good recommendation accuracy of the algorithm through collecting 10000 online reviews information from the mobile channel of Amazon.
Artinian bimodule with quasi-Frobenius bimodule of translations

Abstract The possibility to generalize the notion of a linear recurrent sequence (LRS) over a commutative ring to the case of a LRS over a non-commutative ring is discussed. In this context, an arbitrary bimodule A M B over left- and right-Artinian rings A and B , respectively, is associated with the equivalent bimodule of translations C M Z , where C is the multiplicative ring of the bimodule A M B and Z is its center, and the relation between the quasi-Frobenius conditions for the bimodules A M B and C M Z is studied. It is demonstrated that, in the general case, the fact that A M B is a quasi-Frobenius bimodule does not imply the validity of the quasi-Frobenius condition for the bimodule C M Z . However, under some additional assumptions it can be shown that if C M Z is a quasi-Frobenius bimodule, then the bimodule A M B is quasi-Frobenius as well.
P <sup>3</sup> S <sup>2</sup>

Speech data publishing discloses users' data privacy, and thus entails more privacy risks for users. Existing work sanitized the content, voice, and, voiceprint of speech data without considering the consistence among these three aspects, and therefore cannot protect users' data privacy. To this end, we propose a practical secure protocol for speech data publishing P3S2, the first attempt towards taking the corrections among the three factors into consideration when it sanitizes users' speech data. To concrete, it designs a three-dimension sanitization that utilizes feature learning to capture the set of characteristics in each dimension, and then sanitizes speech data in each dimension using the learned features. As a result, the correlations among the three dimensions of the sanitized speech data are guaranteed. Furthermore, it utilizes two real world datasets, TED talks and LibriSpeech to evaluate the performance of P3S2 in terms of the data privacy preservation.
Collaborative Practices Through Design and Engineering: The Phases of a Pilot Project to Improve the Human Condition of Vulnerable Communities

This paper presents a pilot project, developed under a research Ph.D. framework, showing that the multiplicity of actors from different branches allow the construction of innovative and framed projects. This dynamic and open process result in knowledge, skills and competences construction among themselves and, above all, their applicability in the context of social intervention. This reality emerges the need for integrated multidisciplinary social agents that are an added value for society, analyzing the capacity of cooperation between two different but complementary scientific areas of projectual nature, although with different methods and the practices, Design, and Engineering. The developed prototypes were analyzed quantitatively and qualitatively allowing the researchers to perceive some restrictions when assembling the different materials tested, for a solution more focused on the usability/mobility bearing in mind issues such as budgetary matters and product symbolic character, its main characteristic, related to the thermal efficacy.
Compact I/O-Efficient Representation of Separable Graphs and Optimal Tree Layouts

Compact and I/O-efficient data representations play an important role in efficient algorithm design, as memory bandwidth and latency can present a significant performance bottleneck, slowing the computation by orders of magnitude. While this problem is very well explored in e.g. uniform numerical data processing, structural data applications (e.g. on huge graphs) require different algorithm-dependent approaches. Separable graph classes (i.e. graph classes with balanced separators of size $$\mathcal {O}(n^c)$$ with $$c<1$$ ) include planar graphs, bounded genus graphs, and minor-free graphs. In this article we present two generalizations of the separator theorem, to partitions with small regions only on average and to weighted graphs. Then we propose I/O-efficient succinct representation and memory layout for random walks in (weighted) separable graphs in the pointer machine model, including an efficient algorithm to compute them. Finally, we present a worst-case I/O-optimal tree layout algorithm for root-leaf path traversal, show an additive (+1)-approximation of optimal compact layout and contrast this with NP-completeness proof of finding an optimal compact layout.
Difference Sets Disjoint from a Subgroup

We study finite groups G having a non-trivial, proper subgroup H and $$D \subset G {\setminus } H, D \cap D^{-1}=\emptyset ,$$ such that the multiset $$\{ xy^{-1}:x,y \in D\}$$ has every non-identity element occur the same number of times (such a D is called a difference set). We show that $$|G|=|H|^2$$ , and that $$|D \cap Hg|=|H|/2$$ for all $$g \notin H$$ . We show that H is contained in every normal subgroup of index 2, and other properties. We give a 2-parameter family of examples of such groups. We show that such groups have Schur rings with four principal sets, and that, further, these difference sets determine DRADs.
Pyramidal region context module for semantic segmentation

Context modeling is widely exploited to enhance semantic correlation in semantic segmentation task. Recent approaches (e.g., OCNet, CCNet and DANet) apply non-local type of network to capture the context information. However, they are not accurate enough for handling scale-varying objects due to that they consider very little local dependencies of the adjacent pixels. In this work, we address the complex scene segmentation problem by combining region dependencies and global contextual information. Motivated by the fact that scale of objects largely varies on images, we design the Pyramidal Region Context Module(PRCM) to handle the neighbor relationship of multi-scale regions. In addition, we adopt a depth-to-space layer(PixelShuffle) to form the Scale Transfer Classifier (STC). Based on the two newly proposed modules, we introduce an end-to-end segmentation network - Pyramidal Region Network(PRNet). We empirically demonstrate the effectiveness of our approach on Cityscapes dataset, the results have shown impressive improvement compared with baselines. Notably, PRNet obtains mean IoU of 81.3 on test set of Cityscapes.
Divisibility Tests Unified: Stacking the Trimmings for Sums

SummaryDivisibility tests are algorithms that can quickly decide if one integer is divisible by another. There are many tests but most are either of the trimming or summing variety. Our goals are to present Zbikowski’s family of trimming tests as one test and to unify the trimming and summing tests. We do the latter by showing, first, that the most effective summing tests, due to Khare, can be derived directly from the Zbikowski’s test and, second, that the best known summing tests—the binomial tests—can be derived from an adapted form of Zbikowski’s tests. We introduce the notion of stacking, the claim that a six-year old would always choose 10 pennies over a dime, and use only basic divisibility properties to achieve our goals.
Distributional results relating to the posterior of a Dirichlet process prior

The aim of this paper is to find distributional results for the posterior parameters which arise in the Sethuraman (1994) representation of the Dirichlet process. These results can then be used to derive the posterior of the Dirichlet process.
Recognition of Human Primitive Motions for the Fitness Trackers

Principle elements of a healthy lifestyle and harmful risk factors caused to cardiovascular diseases are being described. Significance of diurnal monitoring of essential characteristics of trustworthy physical state assessment by a fitness tracker is being shown. It had been shown that assessment of registered physiological parameters has to be performed taking into account the personal data such as age, anthropometric data and level of recommended physical activity. The basic block diagram of the typical fitness tracker and its functions are being described. The overall factors such as sensor placement, type of physical activity and human primitive motion, impacting to the fitness trackers measurement accuracy are being analyzed. The two approaches of human primitive motion detection are being proposed. The first one is based on clusterization procedure of original multidimensional time series with 72,4% accuracy of true detection and the second one performs classification procedure of preprocessed multidimensional time series with 85,5% accuracy of true detection.
Application of Genetic Algorithm in Common Optimization Problems

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.
A privacy-preserving framework for ranked retrieval model

In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17, 2015) studied the problem of rank-based inference of private attributes over web databases. They found that one can infer the value of private attributes of a victim tuple by issuing well-designed queries through a top-k query interface. To address the privacy issue, in this paper, we propose a novel privacy-preserving framework. Our framework protects private attributes’ privacy not only under inference attacks but also under arbitrary attack methods. In particular, we classify adversaries into two widely existing categories: domain-ignorant and domain-expert adversaries. Then, we develop equivalent set with virtual tuples (ESVT) for domain-ignorant adversaries and equivalent set with true tuples (ESTT) for domain-expert adversaries. The ESVT and the ESTT are the primary parts of our privacy-preserving framework. To evaluate the performance, we define a measurement of privacy guarantee for private attributes and measurements for utility loss. We prove that both ESVT and ESTT achieve the privacy guarantee. We also develop heuristic algorithms for ESVT and ESTT, respectively, under the consideration of minimizing utility loss. We demonstrate the effectiveness of our techniques through theoretical analysis and extensive experiments over real-world dataset.
Understanding the cultural concerns of libraries based on automatic image analysis

Purpose Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections. Design/methodology/approach In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns. Findings The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives. Originality/value To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.