Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Blended Experiential Learning Mode of Business Administration Talents in Universities

At present, there are problems with insufficient application of teaching technology and in-depth experience-based learning practice in the cultivation of business management talents in universities, which cannot adapt to the heterogeneous needs of industrial and commercial enterprises for management talents with high-innovation and practical ability in the VUCA era. Based on the emerging technology, this paper proposes to build a blended experiential learning mode whose operating mechanism centered on "case teaching + virtual simulation experiment + immersive practical teaching + industry-university-research joint training", and to take human resource management as an example. The blended experiential learning mode proposes measures to strengthen the construction of a double-qualified teacher team, update teaching concepts, optimize situational teaching content, and innovate intelligent teaching methods.
The addRole-EA: A New Evolutionary Algorithm for the Role Mining Problem

Role Based Access Control is an important feature of cyber security and authorization management. The search of an optimum set of roles and their assignment to users can be modeled as an optimization problem, which is known as the Role Mining Problem (RMP) and has been shown to be NP-complete. Thus, fast heuristics are needed to search for solutions for the RMP in affordable time. This paper proposes a new evolutionary algorithm for the RMP, which is based on iteratively adding and deleting roles, while avoiding deviations from the original user-permission assignment. A range of experiments are presented which indicate that the proposed EA performs well on established benchmarking problems.
Development of a Test Environment for the Evaluation of Human-Technology Interaction in Cockpits of Highly-Automated Vehicles

: This paper presents a technologically independent framework to describe test environments suitable for the examination of the driver take-over task present in highly-automated vehicles. As part of a structural analysis, typical inﬂuencing factors and parameters deﬁning the driver take-over task are discussed. According to literature, existing studies examining the driver take-over task make use of various test environments. However, the comparability of their results is not given without a detailed understanding of these. Hence, based on established literature, a technologically independent framework has been developed which can be used to describe the distinct test environments. It turned out, that the referenced models had to be partially restructured in order to be suitable for the description of such test environments. The focus of the present paper lies on their technical implementation characterized by stimulus materials, which have been holistically examined for the driver take-over task. Since stimulus materials provide the foundation of a speciﬁcation of test environments, this work presents an initial step towards a test speciﬁcation aiming on making results obtained from examinations of the driver take-over task comparable.
New Fitness Functions in Binary Harris Hawks Optimization for Gene Selection in Microarray Datasets

: Gene selection (GS) is a challenging problem in medical applications. This is because of the availability of a large number of genes and a limited number of patient’s samples in microarray datasets. Selecting the most relevant genes is a necessary pre-processing step for building reliable cancer classiﬁcation systems. This paper proposes two new ﬁtness functions in Binary Harris Hawks Optimization (BHHO) for GS. The main objective is to select a small number of genes and achieve high classiﬁcation accuracy. The ﬁrst ﬁtness function balances between the classiﬁcation performance and the number of genes. This is done by using a weight that increases linearly throughout the optimization process. The second ﬁtness function is applied across two-stages. The ﬁrst stage optimizes the classiﬁcation performance only while the second stage takes into consideration the number of genes. K-nearest neighbor (K-nn) is used to evaluate the proposed approaches on ten microarray data sets. The results show that the proposed ﬁtness functions can achieve better classiﬁcation results compared with the ﬁtness function that takes into account only the classiﬁcation performance. Besides, they outperform three other wrapper-based methods in most of the cases. The second ﬁtness function outperforms the ﬁrst ﬁtness function across most of the datasets based on classiﬁcation accuracy and the number of genes.
Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0

Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.
Improving Word Association Measures in Repetitive Corpora with Context Similarity Weighting

Although word association measures are useful for deciphering the semantic nuances of long extinct languages, they are very sensitive to excessively formulaic narrative patterns and full or partial duplication caused by different copies, edits, or fragments of historical texts. This problem is apparent in the corpora of the ancient Mesopotamian languages such as Sumerian and Akkadian. When word associations are measured, vocabulary from repetitive passages tends to dominate the top-ranks and conceal more interesting and descriptive use of the language. We propose an algorithmic way to reduce the impact of repetitiveness by weighting the co-occurrence probabilities by a factor based on their contextual similarity. We demonstrate that the proposed approach does not only effectively reduce the impact of distortion in repetitive corpora, but that it also slightly improves the performance of several PMI-based association measures in word relatedness tasks in non-repetitive corpora. Additionally, we propose normalization for PMI2, a commonly-used association measure, and show that the normalized variant can outperform the base measure in both, repetitive and non-repetitive corpora.
Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

Cold-start problem is one of the main challenges for the recommender systems. There are many methods developed for traditional recommender systems to alleviate the drawback of cold-start user and item problems. However, to the best of our knowledge, in session based recommender systems cold-start session problem still needs to be investigated. In this paper, we propose a session similarity-based method to alleviate drawback of cold-start sessions in e-commerce domain, in which there are no interacted items in the sessions that can help to identify users’ preferences. In the proposed method, product recommendations are given based on the most similar sessions that are found using session features such as session start time, location, etc. Computational experiments on two real-world datasets show that when the proposed method applied, there is a significant improvement on the performance of recommender systems in terms of recall and precision metrics comparing to random recommendations for cold-start sessions.
Comparison of Manual Anthropometry and a Mobile Digital Anthropometric System

: With the progress of technology, new digital shape-analysis tools are being developed for use in several different fields. Innovation and market demand has pushed developers to create a portable 3D scanner. The aim of this research was to perform a comparison of a new portable measuring system for digital measurement of anthropometric dimensions of the body, with the system of manual anthropometry. The results show that the Coefficient of determination (R2) was in 7 measurements over 90%, in 6 measurements over 80%, and in 2 measurements above 74.9%. Cronbach Alpha results of compared variables were all over 90%, which show very strong expected correlations. No significant bias between measurement techniques was shown as Bland-Altman plots showed a good agreement between measurement techniques with a small number of outliers. Results provide high validity and accuracy of the new portable scanner when correctly used. However, methods of 3D body scanning and classical anthropometry should not be regarded as interchangeable as there are differences in initial body positions due to the implementation of measurement protocols. Further work is recommended to make the two methods more interchangeable, with the possible usage of corrective coefficients.
Emerging Named Entity Recognition in a Medical Knowledge Management Ecosystem

In this paper, we present a knowledge engineering project in the medical domain. The objective of the project is to identify recent medical knowledge represented by emerging Named Entities. Hence, we introduce the concept of emerging Named Entities and present our studies on their occurrence and use in medical document corpora. We derive an approach for the emerging Named Entity Recognition utilizing textual and temporal features through Natural Language Processing and Machine Learning and present detailed evaluation results. Furthermore, we present a complementary system design that utilizes emerging Named Entity Recognition support several KE use cases in the medical domain.
Sharing secured data on peer-to-peer applications using attribute-based encryption

ABSTRACT The strong growth of communication and storage gives rise to the significantly increasing demand for collecting, storing, and sharing a large amount of data on networks. This is further enhanced by the data-driven market, where everyone wants to access other parties' data. Data is the bedrock of today's technologies and researchers, especially in machine learning and deep learning. The business value of organizations is also massively data-dependent. Recent studies and industry applications can apply analytic techniques for exploiting data, or Internet users can exchange data on social networks or peer-to-peer (P2P) networks. However, sharing secured data is a challenging problem that attracts much attention from researchers. Sharing secure data with a group of users using P2P applications faces the unavailability problem of peer nodes. Thus users cannot certify and download the protected data. This affects a P2P-based application class of sharing and storing online services or customer-to-customer e-commerce applications. This article proposes a solution for sharing secured data on P2P-based applications using blockchain and attribute-based encryption. The attribute-based encryption guarantees sharing keys among a group of users, while blockchain guarantees keys distribution. We have simulated the proposed solution on the mobile peer-to-peer network that provides services for sharing and storing data securely.
Understanding the Use of Narrative Patterns by Novice Data Storytellers

: Data stories are about communicating data, tailored to a speciﬁc audience, with a compelling narrative. Creating them requires a mix of data science and design skills, which can be difﬁcult for beginners. Patterns can help, as they provide tried-and-tested solutions to commonly occurring challenges. ‘Narrative patterns’ are a particular class of patterns that support data-storytellers in structuring the presentation of data within their story, aiding them in effectively communicating with their audience. Our aim is to understand how such patterns are applied in practice and identify ways they could be of greater use, especially for people new to the ﬁeld. To this end, we conduct a review of 67 data stories, created by both professional data storytellers and by postgraduate university students studying data-science, to analyse their use of narrative patterns. Starting from a collection of narrative patterns from the literature, we explore which patterns are used more often, either on their own or in combination, and which ones beginners struggle with. From the ﬁndings we derive recommendations on how to reﬁne some of the less accessible patterns and for training and tool support, which would allow wider audiences to articulate their data insights effectively.
Geographic Feature Engineering with Points-of-Interest from OpenStreetMap

Although geographic patterns have been considered in statistical modelling for many years, new volunteered geographical information is opening opportunities for estimating variables of the city using the urban characteristics of places. Studies have shown the effectiveness of using Points-of-Interest (PoI) data in various predictive applications domains involving geographic data science, e.g. crime hot spots, air quality and land usage analysis. However, it is hard to find the data sources mentioned in these studies and which are the best practices of extracting useful covariates from them. In this study, we propose the Geohunter, a reproducible geographic feature engineering procedure that relies on OpenStreetMap, with a software interface to commonly used tools for geographic data analysis. We also analysed two feature engineering procedures, the quadrat method and KDE in which we conduct a qualitative and quantitative evaluation to suggest which better translate geographic patterns of the city. Further, we provide some illustrative examples of Geohunter
On Specifying and Analysing Domain Ontologies for Workflows in "Binary Model of Knowledge"

: The main purpose of the present paper is to show how concept languages of the system “Binary Model of Knowledge” can be used for specifying workflow ontologies. The system is under development in the Applied Mathematics and Artificial Intelligence Department of National Research University MPEI (Moscow). In particular, the system includes the language LTS of temporal specification. The language includes the sentences matching the sentences of the Boolean and metric extensions of Allen’s interval logic. For the extended logics we present the complete systems of inference rules (in style of analytic tableaux).
Knowledge Management in Service Desk Environment: An Overview of Methods, Tools and Techniques

: Knowledge management and Service Desk are both important topics for successful companies. Good knowledge management practices in Service Desk can improve the quality of delivered service and therefore improve the competitiveness of the company. Based on the literature research followed by analysis we describe and propose methods, tools, and techniques that are or could be used in the Service Desk environment. The methods, tools, and techniques are described according to the phase of knowledge management and the Service Desk activities. This research paper presents a part of work-in-progress research and represents a good base for follow-up research on this topic.
Evolutionary Large-scale Sparse Multi-objective Optimization for Collaborative Edge-cloud Computation Offloading

This paper proposes evolutionary large-scale sparse multi-objective optimization (ELSMO) algorithms for collaboratively solving edge-cloud computation offloading problems. To begin with, a collaborative edge-cloud computation offloading multi-objective optimization model is established in a mobile environment, where the offloading decision is represented as a binary encoding. Considering the large-scale and sparsity property of the computation offloading model, the restricted Boltzmann machine (RBM) is applied to reduce the dimensionality and learn the Pareto-optimal subspace. In addition, the contribution score of each decision variable is assumed to generate new offsprings. Combining the RBM and the contribution score, two evolutionary algorithms using non-dominated sorting and crowding distance methods are designed, respectively. The proposed algorithms are compared with other state-of-the-art algorithms and offloading strategies on a number of test problems with different scales. The experiment results demonstrate the superiority of the proposed algorithms.
Modelling the expected impact of cigarette tax and price increases under Vietnam’s excise tax law 2015–2020

Background Vietnam’s national tobacco control strategy aims to reduce the rate of smoking among male adults from 45% in 2015 to 39% by 2020. The aim of this paper is to assess what contribution cigarette tax increases under Vietnam’s current excise tax plan can be expected to make to this target, and to discuss what additional measures might be implemented accordingly. Methods This study uses a mix of administrative datasets and predictive modelling techniques to assess the expected impact of tax and price increases on cigarette consumption, tobacco tax revenues and the rate of smoking between 2015 and 2020. Findings The average retail price of cigarettes is estimated to have increased by 16% (sensitivity analysis: 14%–18%) in inflation-adjusted terms between 2015 and 2020, while cigarette consumption is projected to decrease by 5.1% (4.5%–5.5%). The rate of smoking among males is projected to decrease to 42.8% (42.1%–43.6%) compared with the target of 39%. Total tax revenues from cigarettes are projected to increase by 21% (19%–23%), reflecting an extra ₫3300 billion in inflation-adjusted revenues for the government. Conclusion The current excise tax law is expected to have only a modest impact on the rate of smoking in Vietnam, though it has generated tax revenues. If Vietnam is to achieve its tobacco control targets, the government should implement a mixed excise system with a high-specific component to promote public health by raising the price of cigarettes more significantly.
Challenges in Public Participation and Collaboration: A Case Study in Finnish Environmental Decision-making

This position paper introduces ongoing research efforts that addresses the ability of different kinds of organizations and multiple individuals to cope together with complex environmental planning and policymaking problems in the Finnish context. The research question “What kind of challenges are there in the collaborative processes of environmental decision-making and how can they be tackled? ” is approached from the perspectives of the framework of public participation process and the theory of collaborative governance. We use these theories as analytical tools to evaluate how the elements and phases of collaboration processes are conducted in practice and to identify problems that exist in the collaborative processes. This phenomenon is studied through a single case study of environmental planning case from a medium-sized city located in
On the Fly SPARQL Execution for Structured Non-RDF Web APIs

The concept of the Semantic Web, built around the idea of semantically described Linked Data and the data model of the Resource Description Framework (RDF), has become a prominent idea of seamless access to, and integration of, data. The number of tools to translate from non-RDF to RDF-representation of data has since then been ever increasing. However, to this day, numerous Web APIs do offer time critical data only in non-RDF-formats. Examples for this are traffic and public transport live data. Due to its nature, an offline data dump, as mostly generated by RDF lifting translation tools, is not practical, as it becomes inconsistent with the original data quickly. In this paper, we for this present an approach, that, published as a microservice, allows to send semantic queries against the legacy Web interfaces directly, and return the result in RDF. The service API follows the SPARQL 1.1 Query API specification, and also supports federated queries over distributed endpoints, allowing an easy and accessible way for semantically enriched data integration over legacy endpoints.
INFLUENCE OF FATTY MATTERS STABILITY ON SHELF LIFE AND QUALITY OF CRUNCHY TOAST

Vegetable oils are found in three major classes in the market; soft (liquid) oils, hard oils (shortening) and margarine that differ in their blends, physical and chemical properties. Sunflower oil, palm oil shortening, partially hydrogenated soybean oil (HSBO) and soft margarine were used to represent these classes in this study. Crunchy toast recipes (high fat roasted pan bread) were prepared using different ratios of these fatty matters (5,10,15,20 and 25% flour basis) and applying different heat treatments in roasting process (110o C/hr, 140o C/25 min and 170o C/15 min). The partially hydrogenated soy oil (HSBO) showed the best stability and longest product shelf life, where sunflower oil was the lowest stability and worst product shelf life, that is indicated by stability chemical indices (peroxide value, free fatty acid, acid value, iodine value and TBA value) and storage tolerance. Not only the fat type influenced the shelf life but also the ratio of this fat. Moreover, the heat treatment clearly affect the stability and shelf life of the crunchy toast where the highest adding ratio and heat treatment the lowest stability and shelf life of products. Adding 1.5% of crushed herbs seeds (cumin, fennel and blackseed) enhances obviously the stability of oil and shelf life of product
Reactivity of C₃N and C₂H at low temperature : applications for the Interstellar Medium and Titan

Le milieu interstellaire ainsi que certaines atmospheres de corps planetaires, en particulier Titan, un des plus grands satellites du systeme solaire, presentent une grande diversite d'especes chimiques. Cette chimie complexe est tres differente de celle que nous connaissons sur Terre. Pour comprendre les phenomenes globaux qui s'y deroulent, une connaissance des reactions chimiques, de leur vitesse et de leurs produits est requise. A l'aide de la technique CRESU, nous sommes capables de reproduire certaines conditions des milieux les plus froids de l'espace et d'etudier ces reactions.