Reviewerly/exHarmony huggingface dataset:
Reusable Formal Models for Threat Specification, Detection, and Treatment

One of the main challenges in engineering secure software systems is the formalization of threats for the automation of security architecture threat detection, analysis, and mitigation. On top of that, there is a growing need for the development of reusable security solutions to support secure systems engineering at early stages of development. We address this challenge by proposing an integrated approach for threat specification, detection, and treatment in component-based software architecture models via reusable security threat and requirement formal model libraries. Our solution is based on metamodeling techniques that enable the specification of the software architecture structure and on formal techniques for the purposes of precise specification and verification of security aspects as properties of a modeled system. To validate our work, we explore a set of representative threats from categories based on Microsoft’s STRIDE threat classification in the context of secure component-based software architecture development. In addition, we use model-driven engineering techniques for the development of a tool suite to support our approach.
HUMAN ACTIVITY RECOGNITION USING SMART PHONE DATA

Human activity recognition has seen a tremendous growth in the last decade playing a majorrole in the field of pervasive computing. This emerging popularity can be attributed to its myriad of real-lifeapplications primarily dealing with human-centric problems like healthcare and eldercare. Data from thesensors attached to a person can be utilized to train supervised machine learning models in order to predictthe activity being carried out by the person. In this paper we will be using Data available at UCI machinelearning Repository. It contains data generated from accelerometer , gyroscope and other sensors of Smartphone to train supervised predictive models using machine learning techniques like SVM , Random forestand decision tree to generate a model. which can be used to predict the kind of movement being carried outby the person which is divided into six categories walking, walking upstairs, walking downstairs, sitting,standing and jogging .We will be comparing the accuracy of different models using confusion matrix and Kfold cross validation
Acousmatic-Creationism: A creative method for acousmatic music inspired by Chilean poet Vicente Huidobro’s aesthetic theories

This article assembles and summarises the main ideas presented in the doctoral thesis entitled ‘Voice and Poetry as Inspiration and Material in Acousmatic Music’ by the author and describes his idiosyncratic method for acousmatic composition based on Chilean poet Vicente Huidobro’s aesthetic theory, which is a system that aims at creating artistic works by taking materials from reality and combining them in unexpected ways. The objective of this combination is an equilibrium between rationality and intuition in order to obtain a poem independent of the real world, in the sense of a poetic outcome which avoids traditional mimesis. This creative system, known as Creacionismo , has a central role between various other theoretical, artistic and mainly poetic sources informing the author’s creative process. Huidobro’s creative system has been applied by the author to acousmatic composition procedures generating the notion of acousmatic - creationist as a nomenclature for the process. This particular creative strategy balances rationality and intuition within acousmatic composition and places poetry as a driving force in the use of voice, merging artistic practice and theory in a recursive action.
An Empirical Study of Cloud Computing Alignment for Enhancing Enterprise Value

Whether cloud computing, which has a series of high-quality characteristics and is widely used, can bring the expected value enhancement to enterprises, is one of the most current concerns in the field of information system. This paper studied the enterprises in Beijing Zhongguancun Technology Park, the first national high-tech industrial development zone in China, based on the breadth and depth of the alignment of cloud computing and used three indicators as the embodiment of enterprise value, namely enterprise agility, enterprise innovation performance and organizational confidence. Based on the perspective of IT infrastructure capabilities, this paper proposed a theoretical model to study how the breadth and depth of cloud computing alignment affects enterprise agility, enterprise innovation performance and organizational confidence. In order to verify the research model, this paper used the method of questionnaire survey in Zhongguancun Technology Park enterprises to collect data, and performed the structural equation model (SEM) to verify the model. Empirical results showed that both wide and deep alignment of cloud computing can promote enterprise agility, enterprise innovation performance and organizational confidence. This shows that enterprises that have achieved either broader or deeper alignment of business and cloud computing have a stronger ability to gain better efficiency and agility, better innovation performance and more confidence from business stakeholders.
Research on the Course Design of Basic Basketball Teaching Intelligent in Colleges and Universities

In order to apply the intelligent classroom to the basic basketball technology teaching of public physical education in colleges and universities, this research explores the application effect of the intelligent classroom which designs in the basic basketball technology (dribbling, three-step layup, one-handed shoulder shooting) and physical exercise attitude.
PRESENT Hardware Implementation for Concurrent Fault Detection Architecture

Efficient fault tolerant implementation of lightweight cryptography hardware is a main challenge of secure hardware in IoT, RFID and sensor network. In this paper, we propose low cost PRESENT architecture using 80-bit key and 64-bit input data with fault detection capability. Our proposed architecture utilizes different S-Boxes implementations in which both encryption and decryption processes are integrated in one data path. The simulation results of our fault detection structure are compared with previous related works. The results are compared in terms of area, throughput and efficiency. This comparison illustrate that our fault dedetectiontection architecture outperforms related works in the comparison metrics.
Privacy-Preserving Graph Operations for Mobile Authentication

Along with the fast development of wireless technologies, smart devices have become an integral part of our daily life. Authentication is one of the most common and effective methods for these smart devices to prevent unauthorized access. Moreover, smart devices tend to have limited computing power, and they may possess sensitive data. In this paper, we investigate performing graph operations in a privacy-preserving manner, which can be used for anonymous authentication for smart devices. We propose two protocols that allow two parties to jointly compute the intersection and union of their private graphs. Our protocols utilize homomorphic encryption to prevent information leakage during the process, and we provide security proofs of the protocols in the semihonest setting. At last, we implement and evaluate the efficiency of our protocols through experiments on real-world graph data.
Student’s Rating of Contexts for Teaching Data Literacy at School regarding the Context Characteristics relation to everyday life and uniqueness

Preparing young people for their future with digital competencies is an important goal for all educational systems. Competency frameworks already exist in many countries and function as guidelines for teaching at school. A central area is data and information literacy, which also plays a key role in computing (education). As a school relevant topic it should therefore be examined more closely. For any learning activity, the interest in what to learn has a major impact on learning motivation and success. But the development of interests in school subject contents is also affected by its teaching contexts. In this respect, it is an open question whether these contexts should be related close to the everyday life of the students or whether they should be unique to have a positive effect on the situational interest of the students. Results can help selecting motivational teaching contexts and to facilitate the learning process, making it easier for students to acquire the required competencies. This paper presents results of a pilot study (N = 28), which is part of a bigger research project consisting of two main studies. This pilot study belongs to the first main study. We present results on how 7th and 8th graders rated 12 contexts for teaching data literacy regarding the relation to their everyday life or uniqueness. This pilot study primarily serves to test a self developed questionnaire and its comprehensibility to use it in the first main study. It also gives first insights into expected results of the upcoming main study.
A scalable approximate Bayesian inference for high-dimensional Gaussian processes

In this paper, we adopt a Bayesian point of view, based on Gaussian processes, for classifying high-dimensional data. Since computing the exact marginal likelihood remains difficult, if not impossible, for discrete likelihoods and high-dimensional inputs, we introduce two different methods to improve the efficiency and the scalability of Gaussian processes for classification: scalable Laplace approximation and scalable expectation propagation, together with an embedding for dimension reduction. The proposed methods are shown to handle multimodal posteriors and improve the prediction quality, jointly. Various numerical tests on simulated and real data provide a comparison with alternative Bayesian and non Bayesian predictors. More particularly, the comparison study confirms that the ability of our proposed methods to estimate the marginal likelihood efficiently yields better predictions, especially for complex tasks.
Dietary Sequence Recommendations Based on TransFM

The rapid development of the Internet has changed people's lifestyles, and more and more people choose to eat online. The online catering platform for users inevitably needs to provide personalized recommendations for users, followed by fewer articles on dietary recommendations. This paper proposes a combination of GBDT and TransFM for dietary sequence recommendation. The hybrid model uses GBDT to process high-dimensional features of food data and capture high-order feature interactions, and then uses TransFM to combine the content features of food and mine the short-term interests of users. Perform sequence recommendations. After experimental comparison, the model is superior to the general sequence recommendation model, and has achieved good results on multiple performance indicators.
6-DoF Extrinsic Calibration Between a Manipulator and a 2D Laser Scanner

A precise geometric calibration between the subsystems of a robotic system is crucial for its usage and applicability. As mobile robots are already being used in industry and households, the interest in mobile manipulators rises, and the calibration procedures between manipulators and the robot’s exteroceptive sensors becomes necessary. Nevertheless, state-of-the-art calibration procedures between a manipulator and a 2D laser scanner often require complex calibration targets or additional sensors for calibration. As a consequence the practical usage in an industrial context is limited. For this reason, we propose a simple but effective approach for determining the transformation between a manipulator and a 2D laser scanner. The transformation is solved for six degrees of freedom using a 1D calibration target. In this paper, the modeling of the original approach presented in [] is extended to decrease the number of restrictions. Therefore, the objective function was improved leading to better optimization results and the methodology of the proposed approach is presented in more detail. Moreover, additional experiments are carried out to evaluate the accuracy of the extended calibration procedure. The effect of different calibration trajectories and calibration target’s geometries is evaluated additionally.
Research on the characteristics of the growth risk sources of new ventures based on Immune Theory

Abstract Under the background of the national strategy of “mass innovation and entrepreneurship” and the information age, it is very important to use the artificial immune theory to characterize the growth risk sources of new ventures and make them become the quantitative index features available for the model. It can lay the foundation for the construction of risk identification model of new ventures. First of all, through the in-depth analysis of the characteristics of the growth risk of the new venture, the source of the growth risk is defined according to the classification of the growth risk of the new venture; then, through combing and summarizing the relevant research at home and abroad, this paper analyzes the influencing factors of R &amp; D risk source, product risk source, management risk source and social network risk source, verifies and selects through empirical research, and constructs the identification index system on this basis; finally, the growth risk characteristic structure of new ventures based on artificial immunity is constructed, and risk identification indicators are engineered to transform qualitative indicators into quantitative indicators for model experiments. It provides a basic reference for the establishment of the new enterprise growth risk identification model, so as to effectively meet the in-depth decision support, information mining and discovery in the growth process of the new enterprise, thereby promoting the stable and healthy growth of the new enterprise.
MUSICALISATION OF J. BUNYAN’S AND H. SKOVORODA’S LITERARY DISCOURSE

The article deals with the musical space of the artistic heritage of J. Bunyan and H. Skovoroda that has many common features. The general place in the heritage of both writers is reference to solemn church or angelic singing, accompanying the scenes of triumph of the heroes. There are numerous quotations from the Bible psalms, that both writers mastered perfectly. Outplaying of the mythologemes “a man as a musical instrument” and “a world as a musical instrument” became common for both authors. Musical code is expressed in comparison with man’s features and musical sounds; assimilation of the world with a musical instrument, desire to hear “the music of spheres”. The comparison of a man’s emotional impulse with the sounds of musical instruments reveals willingness of the man of the Baroque age for the search of correspondence and for the synthesis of arts in a broad sense. Music as an art differs in the ability to reveal symbols by means of a sound, having a significant influence on the recipient. The analysis of musical component of H. Skovoroda and J. Bunyan’s work demonstrates its precise orientation on musicalisation of writers’ discourse. In the meantime musical theme is represented much wider in Skovoroda’s work than in the work of the English writer. The article introduces J. Bunyan and H. Skovoroda as bright representatives of national variants of baroque aesthetics.
Technical Training for Self-Efficacy Improvement of SIARIP Users at the National Archives of the Republic of Indonesia

SIARIP is an e-government application that organizes and stores records of the National Archives of the Republic of Indonesia. The implementation of SIARIP as e-government is needed to support the National Movement for the Awareness of Orderly Archiving implementation which was initiated by the National Archives of the Republic of Indonesia. The user acceptance of information systems implementation is strongly influenced by several factors including self-efficacy. For a successful SIARIP implementation, it is necessary to increase the self-efficacy of SIARIP's users. The improvement of self-efficacy is done using technical training as one of the sources that influences the self-efficacy, namely verbal persuasion. The study was conducted using quantitative methods using a quasi-experimental approach to the design of experimental group pre-test and post-test. There were 22 respondents involved in this study. They are SIARIP's users in the form of records officers at the National Archives of the Republic of Indonesia. Questionnaire results obtained were processed using SPSS using sign test analysis, with the results of the calculated value of sign test (1-tailed) is worth 0.067. Because the value is more than 0.05, it can be concluded that technical training can not increase the value of self-efficacy.
Wedding Dress Question-Answering System

This paper proposes an augmented reality (AR)-based question-answering system (QAS) for wedding dress, which applies mobile application to wedding market. Techniques of AR and QAS are proposed to increment users' satisfaction of selecting and buying wedding dress. In experiments, the results show that the accuracy of the proposed QAS is 97% and higher than other methods and systems.
WristAR: A Wrist-Mounted Augmented Reality Interface

The use of both smartwatches and augmented reality (AR) has steadily grown in the past decade, however there are few examples of combining these technologies to enhance user experiences. We introduce WristAR, a wrist-worn prototype that supports the use of hands-free AR, removing the need to wear a head-mounted display or hold a smartphone, aiming to give a more natural way to experience AR. The prototype is equipped with a camera and screen, making it possible to run AR applications in a form-factor that emulates a smartwatch. In order to test the concept we built an AR application which allows users to play a simple card game displaying animated AR characters. In a user study with 12 participants, we found that although the device was unfamiliar and has restricted movement, participants suggested that with improvements it could become a viable alternative to current AR interfaces.
Shells within Minimum Enclosing Balls

Addressing the general problem of data clustering, we propose to group the elements of a data set with respect to their location within their minimum enclosing ball. In particular, we propose to cluster data according to their distance to the center of a kernel minimum enclosing ball. Focusing on kernel minimum enclosing balls which are computed in abstract feature spaces reveals latent structures within a data set and allows for applying our ideas to non-numeric data. Results obtained on image-, text-, and graph-data illustrate the behavior and practical utility of our approach.
A fast non-interactive publicly verifiable secret sharing scheme

A non-interactive (t, n)-publicly verifiable secret sharing scheme (non-interactive (t, n)-PVSS scheme) is a method to share a secret among n participants so that only subsets of the participants with at least t elements can compute the secret and anyone, not only the participants of the scheme, can verify the correctness of the shares of participants without interacting with the dealer and participants of the scheme. In this paper, we propose a non-interactive (t, n)-PVSS scheme using the homogeneous linear recursions (HLRs) and prove its security in a standard model. For n ≥ t ≥ 2, our non-interactive (t, n)-PVSS scheme runs faster than Schoenmakers's.
SelfieArt: Interactive Multi-Style Transfer for Selfies and Videos with Soft Transitions

We introduce SelfieArt, an interactive technique for performing multi-style transfer for portraits and videos. Our method provides a simple and intuitive way of producing exquisite artistic results that combine multiple styles in a harmonious fashion. It uses face parsing and a multi-style transfer model to apply different styles to the various semantic segments. This is achieved using parameterized soft masks, allowing users to adjust the smoothness of the transitions between stylized regions in real-time. We demonstrate the effectiveness of our solution on a large set of images and videos. Given its flexibility, speed, and quality of results, our solution can be a valuable tool for creative exploration, allowing anyone to transform photographs and drawings in world-class artistic results.
Evolvable Image Filter based on Rotation Invariant Pixel Correlations

Abstract As one of the most typical nonlinear filters, weighted median filter has considerable filter behaviour by assigning weights. But each pixel has only one weight in the traditional weighted median filter. Correlations between pixel and other pixels under the filtering template are not considered. A novel rotation-invariant image filter model is proposed here which has a stable filtering performance when filtering on rotated and reversed images, symmetry of the correlations is utilized to reduce the complexity. Cartesian Genetic Programming is adopted to evolve image filter operators. Experiments about rotated images show that this rotation invariant model exhibits robust characteristics. Image denoising of different images show that it has better properties of edge or details preservation, efficient noise attenuation.
Bayesian Transfer Learning Between Uniformly Modelled Bayesian Filters

We investigate sensor network nodes that sequentially infer states with bounded values, and affected by noise that is also bounded. The transfer of knowledge between such nodes is the principal focus of this chapter. A fully Bayesian framework is adopted, in which the source knowledge is represented by a bounded data predictor, the specification of a formal conditioning mechanism between the filtering nodes is avoided, and the optimal knowledge-conditioned target state predictor is designed via optimal Bayesian decision-making (fully probabilistic design). We call this framework Bayesian transfer learning, and derive a sequential algorithm for pairs of interacting, bounded filters. To achieve a tractable, finite-dimensional flow, the outputs of the time step, transfer step and data step are locally projected onto parallelotopic supports. An informal variant of the transfer algorithm demonstrates both strongly positive transfer of high-quality (low variance) source knowledge—improving on a former orthotopically supported variant—as well as rejection of low-quality (high variance) source knowledge, which we call robust transfer.
Analogy-making as a Core primitive in the software engineering toolbox

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software.