Reviewerly/exHarmony huggingface dataset:
Blockchain for Security Issues of Internet of Things (IoT)

Today in the data driven world, technology has amazed us and changed our life completely. Internet of Things (IoT) has made our life easy by making conventional devices like air conditioner, mobile phones etc. autonomous and smart by revolutionizing the machine to machine communication. IoT is able to represent the real world digitally through the deployment of smart applications in various domains. These applications generate enormous amounts of data and also require internet connectivity all the time for communication between the devices. As the data is communicated via wireless networks, the major challenge lies in the domain of security such as data confidentiality, data reliability, data authentication, privacy etc. Blockchain can be the missing link for settling privacy, and reliability issues of IoT. The connected devices and their communications can be tracked and stored on the tamper-proof ledger of blockchain to ensure reliability and data security of IoT network.
Constructions of 2-Rotation Symmetric Semi-Bent Functions with Degree Bigger than 2

Semi-bent functions have important applications in cryptography and coding theory. 2-rotation symmetric semi-bent functions are a class of semi-bent functions with the simplicity for efficient computation because of their invariance under 2-cyclic shift. However, no construction of 2-rotation symmetric semi-bent functions with algebraic degree bigger than 2 has been presented in the literature. In this paper, we introduce four classes of 2m-variable 2-rotation symmetric semi-bent functions including balanced ones. Two classes of 2-rotation symmetric semi-bent functions have algebraic degree from 3 to m for odd m≥3, and the other two classes have algebraic degree from 3 to m/2 for even m≥6 with m/2 being odd.
Frequency hopping radio individual identification based on energy spectrum blended subtle characteristics

Abstract For the problem of low recognition rate and large computational complexity of frequency hopping radio stations, a blended subtle characteristic extraction method based on energy spectrum is proposed. Considering that the time-frequency energy distribution is widely used in individual feature extraction, the time-frequency energy spectrum of the frequency hopping signal is obtained by the sparse reconstruction method firstly. Then the fractal theory is used to extract the feature vector set including time-frequency energy spectrum Rayleigh entropy, multi-fractal dimension and difference box dimension with multi-scale block extraction. Finally, the support vector machine classifier is used to train, classify and identify the feature set to realize the individual identification of the frequency hopping station. The recognition performance of the proposed method and the other two methods are compared and verified through the frequency hopping signals of four stations. The experimental results show that compared with the other two methods, the proposed method has higher recognition rate under low SNR and a small number of training samples.
Debreceni útszakaszok zajtérképes vizsgálata

During the research, noise level calculations were performed for 13 numbered roads in and around Debrecen and a noise map analysis was conducted on the roads’ environment. The first examined year was 1995, the last was 2017. In the time interval, annual status was determined based on average daily traffic data. In addition to the road noise values calculated for each section, a noise map was prepared for each survey year. For the evaluation of the results a difference map of the highlighted periods was prepared. Typically, noise level increase from road sources was identified between 1995 and 2017 in a significant part of the studied area (southern and eastern parts of Debrecen and around the newly built roads). Stagnant noise level was diagnosed near the downtown of Debrecen and around the main road No. 4, furthermore in some places noise reduction was caused by the development of the road network (between Debrecen-Jozsa and Hajduboszormeny, between Debrecen and Latokep around road No, 33, and along roads No. 4808 and No. 4814) .
Ring-LWE Public Key Encryption Processor

In this chapter we analyze the $$\mathtt {LPR}$$ ring-LWE public key encryption scheme of Sect. 2.4.1 and design a compact hardware architecture of the encryption processor. From Fig. 2.4 of Sect. 2.4.1 , we see that the $$\mathtt {LPR}$$ encryption scheme is composed of a discrete Gaussian sampler, a polynomial arithmetic (addition/multiplication) unit, a message encoder and a message decoder. In the last chapter we described how to design the discrete Gaussian sampler efficiently. In this chapter we first design a novel polynomial arithmetic unit and integrate it with the discrete Gaussian sampler to realize the ring-LWE public key encryption processor.
Guided Sparse Feature Matching Via Coarsely Defined Dense Matches

This paper addresses the problem of finding semi-dense feature correspondences with better than quarter-pixel accuracy across stereo images in an efficient manner. Having a large number of correspondences is critical for many computer vision applications as more observations allow to better model application parameters. Also, performance of many applications can greatly improve with correspondences of at least quarter-pixel accuracy. However, defining a large number of quarter-pixel accurate feature correspondences effectively is still a challenging task. Specifically, dense matching attempts to find dense correspondences; while it is attractive to have a feature correspondence per pixel in a given image, such approaches are typically computationally heavy with at most half-pixel accuracy. In contrast, sparse feature matching approaches are computationally less expensive and generally benefit from better than quarter-pixel accuracy for the defined correspondences, but, they define correspondences for only a limited number of pixels in an image. In addition, they tend to have a higher rate of mismatches as they do not consider spatial smoothness at their core. This paper introduces a new framework to address the above shortcomings, aiming to significantly increase the number of correct matches with at least quarter-pixel accuracy, while keeping the computational cost smaller than that of dense matching alone.
Research on Principle of IT Project Valuation

This article analyzes the IT project valuation mechanism, and proposes that the basis of IT project valuation is to assess its value and price. On the basis of that, it specifically explains the conception, composition and characteristic of the IT project value. The article also discusses the IT project price as well as the generation of its value and price.
Structural features of complex hydrochemical systems

The set of non-conservative hydrochemical parameters is considered as a complex system, which displays collective behavior. It is found that the collective behavior is described by the power relation between the time variability (the standard deviations) and the average concentrations of different hydrochemical parameters in the scale range 100 – 0:0001 mg/kg. The exponent can be 0:7 – 0:9. Power law scaling is the mathematical expression of self similarity and fractality. The complex systems of nonconservative chemical parameters have a structure that can be characterized by exponent, normalization coefficient, standard error, correlation coefficient, and by sharp deviations of the individual parameters from the regression line and from the most probable average and standard deviation values, if any. It is shown with specific examples that changes in the hydrochemical systems structure are the result of the manifestation of biogeochemical processes and the dynamics of water. Regression analysis of collective behavior of complex hydrochemical systems is one of the examples of the use of modern information technologies based on the methods of system analysis.
Discovering structure in multiple outcomes models for tests of childhood neurodevelopment

Abstract Bayesian model–based clustering provides a powerful and flexible tool that can be incorporated into regression models to better understand the grouping of observations. Using data from the Seychelles Child Development Study, we explore the effect of prenatal methylmercury exposure on 20 neurodevelopmental outcomes measured in 9‐year‐old children. Rather than cluster individual subjects, we cluster the outcomes within a multiple outcomes model. By using information in the data to nest the outcomes into groups called domains, the model more accurately reflects the shared characteristics of neurodevelopmental domains and improves estimation of the overall and outcome‐specific exposure effects by shrinking effects within and between domains selected by the data. The Bayesian paradigm allows for sampling from the posterior distribution of the grouping parameters; thus, inference can be made about group membership and their defining characteristics. We avoid the often difficult and highly subjective requirement of a priori identification of the total number of groups by incorporating a Dirichlet process prior to form a fully Bayesian multiple outcomes model.
Discriminative Joint Vector And Component Reduction For Gaussian Mixture Models

We introduce a discriminative parametric vector dimensionality reduction algorithm for Gaussian mixtures that is performed jointly with mixture component reduction. The reduction algorithm is based on the variational maximum mutual information (VMMI) method, which in contrast to other reduction algorithms, requires only the parameters of existing high order and high dimensional mixture models. The idea behind the proposed approach, called JVC-VMMI (for joint vector and component VMMI), differs significantly from traditional classification approaches that perform separately dimensionality reduction first, and then use the low-dimensional feature vector for training lower order models. The fact that the JVC-VMMI approach is relieved from using the original data samples admits an extremely efficient computation of the reduced models optimized for the classification task. We report experiments in vowel classification in which JVC-VMMI outperformed conventional Linear Discriminant Analysis (LDA) and Neighborhood Component Analysis (NCA) dimensionality reduction methods.
Graphic and Numerical Evolution of a Bonus-Malus System via Markov Chain Models

Abstract This paper describes an online solution for visualizing the simulation of a discrete time Markov chain using an animated graph. Based on the D3.js library, the development of this solution offers the user the choice between a presentation based on the concept of a probabilistic graph oriented and a presentation of the transitions in a matrix framework. As an application the solution helps to describe the dynamics of individual policyholders in a Bonus-Malus system of automobile insurance.
Map-Reduce Based Generic Basis of Association Rules Mining from Big Bata

Mining big data poses computational and memory challenges because of the astonishing rate of data generation when addressed by traditional mining methods. To deal with such problems we can take advantage of parallel programming such as MapReduce which permits parallel processing in massively distributed environment. In this paper, we address the issue of mining association rules from big datasets in such environments. For this, we introduce two contributions. The first one consists on exploiting irreducible paradigm for attributes reduction. The second one is to introduce a new generic parallel algorithm called DGARM for mining generic association rules from big data. We carried out exhaustive experiments over real world datasets to illustrate the efficiency of DGARM for large real world datasets.
Discrete Gaussian Sampling

In this chapter we propose an efficient hardware implementation of a discrete Gaussian sampler for ring-LWE encryption schemes. The proposed sampler architecture is based on the Knuth-Yao sampling Algorithm [10]. It has high precision and large tail-bound to keep the statistical distance below $$2^{-90}$$ to the true Gaussian distribution for the secure parameter sets [6] that are used in the public key encryption schemes [12, 17].
Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy

Arabic calligraphy (AC) is central to Arabic cultural heritage and has been used since its introduction, with the first writing of the Holy Quran, up until the present. It is famous for the artistic and complicated ways that letters and words interweave and intertwine to express textual statements – usually quotations from the Quran. These specifications make it probably the hardest of all human writing systems to read. Here, we introduce the challenge of reading Arabic calligraphy using artificial intelligence (AI), a challenge that combines image processing and understanding of texts. We have collected a corpus of 1000 AC images along with annotated quotations from the Quran, pre-processing the images and identifying individual letters using detection methods based on maximally stable extremal regions (MSERs) and sliding windows (SWs). We then collect the identified letters to form bags of extracted letters (BOLs). These BOLs are then used to search for possible quotation from the corpus. Our results show that MSERs outperforms SWs in letter detection. Furthermore, BOL-matching is better than word generation in predicting the correct quotation, with the correct answer found in the list of 10 topmost matches for more than 74% of the 388 test examples.
Mikhail Prishvin's «Diaries» as a publishing project

The research centers upon the bibliographical study of the history (1991–2017) and prehistory (1957–1990) of M. M. Prishvin’s «Diaries» (1905–1954) publication recognized as the longest (18 volumes) diaries in Russian literature. In modern science Prishvin’s «Diaries» are studied in a number of aspects: as a historical and cultural chron­icle of the country in 1905–1954; the writer’s self-consciousness and creative laboratory; a fiction text in the system of its motives, literary and philosophical contexts, as well as from the point of view of its publishing fate which is narrowly understood as a fragmentary history of its publication. The paper novelty is due to, first, reconstruction of the history and prehistory of the «Diaries» publication, and second, the system analysis of the publication history in connection with the changing economic models of publishing business, types of publishing houses, their repertoire, strategies, and features of the editorial work during the publication of the collected works. Moreover, the author distinguishes three types of ego-texts in Prishvin’s works (sketch books, diary, and diary books) and, accordingly, different publication strategies. The study reveals that within the prehistory of the «Diaries» publication there were two main approaches to their publishing: first, they were published in shortened versions (1986); second, in fragmentary versions based on the thematic or chronological principle, most often in a journal variant. Prishvin’s «Diaries» are considered in the context of the writer’s whole collected works: the pre-Soviet («Znanie Publishing House», 1912–1914) and the Soviet («Gosizdat», 1927–1930, 1929–1931; «Goslitizdat», 1935–1939; «Khudozhestvennaya literature», 1982–1986) periods. The history of Prishvin’s «Diaries» publication in the post-Soviet period is described as a collective book project carried out by the efforts of five state and non-state publishing houses: «Moskovskii Rabochii» (1991–1995), «Russkaya kniga» (1999–2004), «ROSSPEN» (2012); «Novyi Khronograf» (2013–2014); and «Rostok» (2006–2017). The author demonstrates the «Diaries» connection with the repertoire and strategies of these publishers. After the reconstruction of the history and prehistory of Prishvin’s «Diaries» publication from the initial fragments to full print and electronic versions, the author convincingly proves that this long-term collective book project belongs to the local history of the Russian publishing industry in the XX–XXI centuries.
Semantically Driven Auto-completion

The Bloomberg Terminal has been a leading source of financial data and analytics for over 30 years. Through its thousands of functions, the Terminal allows its users to query and run analytics over a large array of data sources, including structured, semi-structured, and unstructured data; as well as plot charts, set up event-driven alerts and triggers, create interactive maps, exchange information via email and instant messaging, and so on. To improve user experience, we have been building question answering systems that can understand a wide range of natural language constructs for various domains that are of fundamental interest to our users. Such natural language interfaces, while exceedingly helpful to users, introduce a number of usability challenges of their own. We tackle some of these challenges through auto-completion. A distinguishing mark of our auto-complete systems is that they are based on and guided by corresponding semantic parsing systems. We describe the auto-complete problem as it arises in this setting, the novel algorithms that we use to solve it, and report on the quality of the results and the efficiency of our approach.
The development of digital video applications for deaf students

Abstract The purpose of this study was to obtain a picture of the development as well as determine the practicality and effectiveness of digital video applications for deaf students. This research method usinganalysis, design, development, implementation, and evaluation (ADDIE).The study was conducted in extraordinary high school part B Ciamis. Samples are deaf student’s as many as five people. Data collection technique used observation, interview and assessment skills. The assessment process digital video applications is done by three expert judgment namely media experts, expert learning materials, and expert skills that the results show that the development of digital video applications for deaf students should pay attention to several aspects, namely, the speed of time, materials selection and image objects, font selection and color for the text narration, and must match the content of learning materials so that they can help students to gain an understanding in terms of both theory and practice. The result of the development of digital video learning applications for deaf students This has advantages that make it easier to understand the deaf students are with their material content direct overview and explanation in the form of narrative text and sign language so as to make a special attraction for the students to learn. This digital video applications can help replenish the skills for deaf students in the classroom so that learning is not only with the conventional method.
Elementary Methods for Reliability Evaluation

This chapter describes elementary approaches to network reliability evaluation. The first method is based on making a list of all $$2^n$$ binary network states. This method is very good but is applicable only to rather small networks with $$n=4-6$$ elements. For $$n>6$$ we need a computer to make the list of all states. Next in line is so-called Crude Monte Carlo (CMC). It consists of performing a series of M lottery-type random experiments of modelling element states and analysing the network state (UP/DOWN) as the result of this experiment. If we observe $$M_1$$ times the network in DOWN state, $$M_1/M$$ is the CMC estimate of network DOWN probability. CMC has, however, an algorithmic complication in identifying the random lottery result as the network DOWN or UP state. We describe in Sect. 4.5 a technique called DSS (disjoint set structures) for simple and fast identification of network state. During the first reading of this chapter, Sect. 4.5 can be omitted and studied only when the reader starts writing computer codes. This chapter contains also estimation of rel.err. of the CMC, and demonstrates how to analyse network reliability in time (dynamic reliability).
Acquisition method and calibration application on hyperion hyperspectral reflectance

As the most commonly used hyperspectral data source, the acquisition method of Hyperion hyperspectral reflectance became more and more important. In this study, a batch atmospheric correction method based on Second Simulation of a Satellite Signal in the Solar Spectrum radiative transfer code (6SBAC) has been presented. The 6SBAC was used MODIS synchronous atmospheric data products to obtain meteorological parameters, and set up automatic input-output parameter module to obtain Hyperion reflectance data. At the same time, using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Quick Atmospheric Correction(QUAC) atmospheric correction methods on Hyperion satellite images, the results was compared with the synchronous hyperspectral data measured in Dunhuang test site. The authenticity of the obtained hyperspectral reflectance was verified by MODIS surface products. The results showed that the hyperspectral reflectance obtained by the three kinds of atmospheric correction methods were consistent with the measured reflectance in trend. Despite the compromises in different wavelength regions 6SBAC is found to be a better corrector method for the Hyperion hyperspectral reflectance acquisition. The precision of the 6SBAC obtained hyperspectral reflectance can meet the application requirements and improve the acquisition efficiency of Hyperion hyperspectral reflectance data.
Proposing a Hand-Tracking Device using a Tangential Force Mechanical Sensor

Conventional hand-tracking devices are constructed with inertial measurement units, bending sensors, and optical technologies. However, these are limited by their high-cost and environmental factors. In this research, a hand-tracking device using a tangential force mechanical sensor for use in Immersive Virtual Environments is proposed.
A Feature-Based Approach to Develop Digital Board Games

Several types of development strategies are available to provide digital games in a reusable way. However, the idea of a "one-size-fits-all" architecture for digital games can be problematic, being preferable to build dedicated architectures for specific game genres. This paper proposes the development of feature-based artifacts for the production of digital board games. It presents a subdomain game architecture that represents configurable features of core concepts related to board games (the game model and controller), and implements feature artifacts capable of being executed in distinct game clients (the game view). For validation purposes, two types of classic board games, together with a proposed web client for board games, were developed, consolidating as a result a software product line approach to develop classic board games.
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference

Abstract An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.