Reviewerly/exHarmony huggingface dataset:
POSTER: LHSA: Lightweight Hardware Security Arbitrator

Hardware security components provide a Root-of-Trust (RoT) environment for critical system operations and bring in a substantially high level of security guarantee since it is protected from various software-based vulnerabilities and side channel attacks. There are a few state-of-art hardware security components which are prevalent in modern digital devices such as crypto engines, key management cores, and random number generators among others. However, most hardware security components are not exposed to end-user applications and their usage and benefits are severely limited by the policies imposed by device vendors. The reason behind the strict restriction is to reduce the attack surface and harden the hardware security components. However, application is the most vulnerable attack surface and requires utmost protection. Hence, it is imperative to provide a secure mechanism through which applications can fully leverage the hardware security components. In this paper, we propose the Lightweight Hardware Security Arbitrator (LHSA) which opens up a path for applications to fully utilize hardware security components without jeopardizing hardware security hardening. LHSA creates virtualized hardware security components for each application and applications can utilize the full hardware security functionalities in a securely isolated environment. We have built a prototype using a commodity Windows platform on ARM SoC (System-on-Chip) environment and we demonstrate its practicality and security benefits.
An Experiment on the Impact of Information on the Trust in Artificial Intelligence

Artificial intelligence (AI) has made considerable progress in a variety of fields and is suggested to do as well or better than many experts, creating great expectations about its potential to improve decision-making. While much progress has been made in refining the accuracy of algorithms, much remains to determine on how these algorithms will influence decision-makers, especially in life or death decisions such as in medicine. In such fields, human experts will remain for the foreseeable future the ultimate decision-makers. Literature suggests that reliance on algorithms by decision-makers may be influenced by the accuracy of algorithm and by the information on how the algorithm reached its conclusions. The objective of this paper is to determine the propensity to influence pathologists' decision-making using algorithmic expertise and information on AI algorithm accuracy and model interpretability. To test our hypotheses, we will conduct an online, quasi-experimental survey study with 120 respondent pathologists. Each participant will provided with a series of prostate cancer samples and asked to assess the Gleason grade. Our hypothesis is that increasing the level of information will lead to increased reliance in automated systems. This research will provide insight into trust in AI: first, the extent to which pathologists trust AI advice; second, the extent to which each type of information contributes to trust.
Challenges to Records and Information Management in the Fourth Industrial Revolution

The fourth industrial revolution has created many challenges for records and information management. This chapter provides an overview of the field of records management and describes the fourth industrial revolution and its impact. Further, it identifies several challenges to the profession of records manager, such as the effect of cloud computing, big data, mobile devices, and financial and ethical concerns. The author investigates how records managers can keep up to date with the changes in their profession by training and continuing education opportunities. Finally, the author discusses some possible solutions to the many challenges records managers face so that they can do their jobs effectively.
Forms and methods of organizing extracurricular musical and educational work

The article is devoted to the review and analysis of forms and methods of organization of extracurricular music-educational work, which would promote the development of children, the formation of their musical culture, creative attitude to life and art. As well as these forms and methods will contribute to the development of patriotism and students’ aesthetic. Extracurricular music-educational work complements music lessons and aims to expand the musical horizons of children, deepen their knowledge, as well as improve performance skills and abilities. The organization of extracurricular musical activities involves compliance with certain guidelines. The success of a variety of work combined to develop musical skills depends largely on how well students master and feel the need for different musical activities. The obtained musical skills of the participants have a way out: together they prepare themed evenings, concerts, perform at holidays and reviews. This gives them the opportunity to feel the need and importance of the ability to own a musical instrument. In the process of teaching singing, the teacher solves many problems related to the development of students' voices and the acquisition of vocal and choral skills and abilities necessary for artistic expression. With choral singing activists, you can prepare an article for the school wall newspaper, in which to talk about the importance of creating a choir, the prospects for its development and immediate tasks; draw up a colorful poster announcing the organization of the choir, where to indicate the days and hours of listening to all who wish to sing.
A Study on Privacy Preserving Technology Using Min-Hash Based Similarity Measurement Method

Recently, Traditional Methods of measuring the similarity have been time-consuming and costly as the size and area of data increase. Border proposed a Min-Hash efficiently estimates the similarity between two signatures represented two-sets as the connotated form. Min-Hash is widely used in plagiarism prevention, graph and image analysis, and genetic analysis. However, raw data is encrypted but exposure to keys due to frequent use of keys for decryption poses security challenges. In particular, exposure to data about users at large sites such as Facebook and Amazon causes serious damage. More recently, studies of new fourth-generation encryption technologies that can protect user-related data without using the keys needed for encryption have drawn attention. Also, data clustering technology that uses encryption is drawing attention. Thus, among the various clustering methods, this paper presents model using Rushell and Rao similarity for preserving privacy by using RSA homomorphic encryption and estimates efficiently it by using Min-Hash.
Sparse Convolution Subspace Clustering

The real-world high-dimensional data lie on low-dimensional manifolds embedded within the high-dimensional space. Therefore, clustering in high-dimensional spaces is a difficult problem. Subspace-based clustering methods are proposed to project the high dimensional data into a low-dimensional space, and then find clusters in this low-dimensional subspaces of the high dimensional data, instead of finding clusters in the entire feature space. In this work, we propose a subspace clustering method called Sparse Convolution Subspace Clustering (SCSC) which is inspired by Sparse Subspace Clustering (SSC). SSC is to find a sparse representations of a data point in terms of other points while SCSC tries to find a sparse convolutional representations of a data point in terms of other points. A group optimization method based alternating direction method of multipliers (ADMM) is used to solve the sparse convolutional representation problem. It should be pointed out that SSC is a special case of SCSC while the convolution kernel size is set as 1 ×1. The experimental results on face data show the effectiveness of the proposed SCSC.
Teaching Innovation Practice of “Advanced Office Application” Based on Internet Plus

Advanced office application (AOA) is a general elective course for non-computer majors. It is instrumental and vocational skill operation course. In the face of the “Internet plus” teaching background and the compression of curriculum hours and the undiminished teaching content, it is an important teaching problem to how to solve the phenomenon of students’ submission to light theory and copy work and how to use the online learning platform and offline classroom teaching to better cultivate students’ innovative ability, independent thinking and problem-solving ability, and improve the efficiency of office work and occupation moral accomplishment of students. The teachers have carried out teaching innovation practice in the following aspects: changing the teaching mode of the course, taking the students’ knowledge, skills, operation and quality training as the centre, carrying out the teaching design, integrating into the curriculum, revising the syllabus of the ideological and political elements, and teaching assessment methods, and at the same time, paying attention to the Internet plus students’ personalized guidance and assistantship training, forming a good course. The service system mode, with the learning atmosphere of helping more and bringing more, cultivates the students’ ability of team cooperation, mutual assistance, innovation and entrepreneurship. Its teaching innovation practice effect is good, and the implementation way has good promotion and application value.
Metaphorical truth and neopragmatic realism of Richard Rorty

The subject of this research is the examination of neopragmatic realism of Richard Rorty through the prism of the concept of metaphorical truth of Paul Ricoeur. The key goal lies in the attempt to fill the &amp;ldquo;black box of reality&amp;rdquo; in the philosophy of R. Rorty with the methodological instruments derived from the concept of metaphorical truth of P. Ricoeur. Having dedicated certain time to describing the core ideas of R. Rorty, the author analyzed the methodology of P. Ricoeur regarding the problem of reality and the cognitive opacity of language characteristic to the system of the American philosopher. The author seeks to determine the common and contradictory aspects in methodology of both philosophers for the purpose of conducting the substitution of the elements of Rorty&amp;rsquo;s philosophy that is organic for the entire logical construct, concluding on the cognitive opacity of language and elements of Ricoeur&amp;rsquo;s thoughts that lead to backward reasoning. The crucial element is the idea of &amp;ldquo;physis&amp;rdquo; borrowed by Ricoeur from Aristotle for ontological substantiation of the concept of metaphorical reality. Namely this idea that becomes the key to recoding of the philosophical system of R. Rorty to what can be called full realism in the philosophical sense. As a result of application of the idea of &amp;ldquo;physis&amp;rdquo; to Rorty's philosophy, the author finds the way to saturate the concept of reality with certain ontological content; however, this content is not susceptible to structural description, and thus does not allow solving the problem of cognitive opacity of language characteristic to Rorty's philosophy. Nevertheless, the application of P. Ricoeur&amp;rsquo;s concept of metaphorical truth to the philosophical system of R. Rorty in the context of ontological problem of accessing reality is the original tactic of reasoning that has not been previously implemented within the scientific and philosophical circles. Although one of the advanced hypothesis that suggests the possibility of substantiation of the cognitive opacity of language was being refuted, the concept of P. Ricoeur allowed saturating the concept of reality of the American philosopher with the ontological content.
Autonomous Vehicle Standards under the TBT Agreement: Disrupting the Boundaries?

Products that incorporate AI will require the development of a range of new standards. This chapter uses the case of connected and autonomous vehicles (CAVs) standards as a window to explore how this “disruptive innovation” may alter the boundaries of international trade agreements. Amid the transition to a driverless future, the transformative nature of disruptive innovation renders the interpretation and application of trade rules challenging. This chapter offers a critical assessment of the two systematic issues – the goods/services boundaries, and the public/private sector boundaries. Looking to the future, regulations governing CAVs will become increasingly complex, as the level of systemic automation evolves into levels 3-5. The author argues that disruptive technologies have a greater fundamental and structural impact on the existing trade disciplines.
Digital Literacy in the Aspect of Continuing Teacher's Education

The article discusses the issue of digital literacy in the aspect of continuing education. Theoretical analysis of the problem is given. Diagnostics were carried out to determine the main problems of insufficient digital literacy of teachers. Recommendations have been developed to improve digital literacy in the teacher's continuing education system.
Improve Transparency and Credibility of Students' Educational Records using School Operated Blockchain System

Currently, due to the physical distance and complex nature of the educational environment, there is room for falsification of educational material for graduate studies. Along with the blooming of Blockchain and Cryptocurrency, an increasing number of researches focus on the transparency brought by this distributed ledger technique, which may help solve this problem. This paper aims to provide a further understanding of Blockchain technology in providing immutable and transparent records for students' educational background. Two main applications in education, called Indorse and Blockcerts were presented and we developed a new application SchooIOperated-Educational-Blockchain to provide more accurate and various information for students' in-school performance and store them securely and transparently on the Blockchain.
Inter-Dimensional Correlations Aggregated Attention Network for Action Recognition

Recently, 3D Convolutional Neural Network (3D-CNN) models with attention mechanisms have been widely studied in action recognition tasks. Although most of these methods explore the spatial, temporal and channel attention for action recognition, the inter-correlations over spatial, temporal and channel are not fully exploited. In this paper, we introduce a novel inter-dimensional correlations aggregated attention (ICAA) network that extracts inter-correlations between two dimensions in spatial, temporal and channel, and inter-spatial-temporal-channel correlations to obtain more comprehensive correlations. The proposed ICAA module can be wrapped as a generic module easily plugged into the state-of-the-art 3D-CNN models as well as multi-stream architectures for video action recognition. We extensively evaluate our method on action recognition tasks over UCF-101 and HMDB-51 datasets, and the experimental results demonstrate that adding our ICAA module can obtain state-of-the-art performance on UCF-101 and HMDB-51, which has the performance of 98.4% and 81.9% respectively, and achieve significant improvement compared against original models.
Automated Reasoning with Legal Ontologies: A Case Study on Brazilian Councilwoman Marielle Franco’s Murder

In the Semantic-Web endeavor, legal ontologies are object of intense study given the syntactic and semantic anomalies that may be encountered in legal knowledge, such as conflicting norms governing the same situation. Nevertheless, little study is reported on legal ontologies' application and reasoning on legal cases. Therefore, we present in this paper an instantiation of a legal ontology for the unsolved case of the murder of Brazilian councilwoman Marielle Franco. Additionally, we describe a prototype to make inferences over criminal types and penalties based on the instantiated ontology.
Structured argumentation dynamics

Abstract This paper develops a logical theory that unifies all three standard types of argumentative attack in AI, namely rebutting, undercutting and undermining attacks. We build on default justification logic that already represents undercutting and rebutting attacks, and we add undermining attacks. Intuitively, undermining does not target default inference, as undercutting, or default conclusion, as rebutting, but rather attacks an argument’s premise as a starting point for default reasoning. In default justification logic, reasoning starts from a set of premises, which is then extended by conclusions that hold by default. We argue that modeling undermining defeaters in the view of default theories requires changing the set of premises upon receiving new information. To model changes to premises, we give a dynamic aspect to default justification logic by using the techniques from the logic of belief revision. More specifically, undermining is modeled with belief revision operations that include contracting a set of premises, that is, removing some information from it. The novel combination of default reasoning and belief revision in justification logic enriches both approaches to reasoning under uncertainty. By the end of the paper, we show some important aspects of defeasible argumentation in which our logic compares favorably to structured argumentation frameworks.
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems

Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain environments, but the tools for formally analyzing how this uncertainty propagates to NN outputs are not yet commonplace. Computing tight bounds on NN output sets (given an input set) provides a measure of confidence associated with the NN decisions and is essential to deploy NNs on safety-critical systems. Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs. However, the bound looseness causes excessive conservatism and/or the computation is too slow for online analysis. This paper unifies propagation and partition approaches to provide a family of robustness analysis algorithms that give tighter bounds than existing works for the same amount of computation time (or reduced computational effort for a desired accuracy level). Moreover, we provide new partitioning techniques that are aware of their current bound estimates and desired boundary shape (e.g., lower bounds, weighted <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -ball, convex hull), leading to further improvements in the computation-tightness tradeoff. The paper demonstrates the tighter bounds and reduced conservatism of the proposed robustness analysis framework with examples from model-free RL and forward kinematics learning.
APLIKASI AUGMENTED REALITY UNTUK PEMASARAN PRODUK BAKERY MENGGUNAKAN METODE MARKERLESS BERBASIS ANDROID

Augmented Reality technology contributes mainly to the field of marketing. Being a digital technology that can be used for marketing with the concept of a realworld object designed in 3D then displayed virtually through a smartphone screen so that it can be seen as if there was an original object being seen. Carrrefour Kepri Mall is a modern retail company, as a shopping destination, stay and play with a lifestyle concept, affordable and complete. However, the concept of a company with an unbalanced company work system. It can be seen that the marketing process that occurs in the field is still carried out using manual brochures which only include the name and price. Judging from the company's concept so that Augmented Reality technology becomes relevant to be applied to the company's work system. It is hoped that in the future by applying Augmented Reality technology the marketing process can be more effective because it is not direct and consumers can directly see existing products in 3D without having to see the product directly.
Stability Derivative Identification Using Adaptive Robust Extended Kalman Filter for Multirotor Unmanned Aerial Vehicle (M-UAV)

The invention of Unmanned Aerial Vehicle (UAV) in the early 1900 for military purposes and UAV applications in commercial purposes afterwards in the early 2000 accelerate its research in many engineering fields. Multirotor UAV such as quadrotor is usually unstable without a flight controller. Consequently, a proper and accurate model of the UAV dynamics is essential for its system stability. System identification allows the researchers to create an accurate parameter to the mathematical model of a dynamic system based on measured data. This work emphasizes in designing a robust and adaptive filter to develop an accurate mathematical model based on Newton-Euler method which includes aerodynamic drag and moment which are necessary in determining the correct model prediction. The focus of the present work is mainly on Kalman filter development for parameter estimation. While Kalman filter is only efficient in linear problem, an extended version of the filter itself deals with the nonlinear problem in which most real problem is actually nonlinear. This work investigates the performance of the extended version of the Kalman filter relative to parameter estimation. The performance of the filters are evaluated based on their estimation with the actual recorded flight data and presented based on data overlapping using Root Mean Square Error (RMSE). Ardupilot APM is used to acquire the actual flight test data and MATLAB is utilized to carry out the state estimation. To evaluate the performances of the filters, Goodness of Fit (GOF) approach was used. It is found that the GOF index of the present approach is 0.853 which is 25% higher than that of the Robust Extended Kalman Filter approach for the present flight test result.
Parameter Identification of Horizontal Flexible Plate System Using Cuckoo Search Algorithm

The flexible structure offers various advantages such as lightweight, efficient, fast system response and low energy consumption. However, this structure produces too much vibration which lead to system failure. This thesis presents a modelling of the horizontal flexible plate system based on cuckoo search algorithm. The aim of this research is to eliminate vibrations produced by the flexible plate structure in order to preserve the performance and lengthen the life of the system. Initially, the system models are developed based on the vibration data collected experimentally. The dynamic models were developed based on auto-regressive with exogenous (ARX) using system identification technique. The system identification technique applied in this study to develop the mathematical models of the system are the conventional technique known as recursive least squares (RLS) and swarm intelligence algorithm known as cuckoo search algorithm (CS). Then, the developed system models were evaluated and validated by mean squared error (MSE), correlation test and also pole-zero diagram. The performance of developed model was compared between the conventional and swarm intelligence algorithms. The best system model was verified based on lowest MSE, within 95% confidence level of the correlation test and high stability of the pole-zero diagram.