International Institute of
A Data Oriented Approach to the Problem of Power Grid Non-Technical Losses in Developing Countries
Grant, Leonardo *; Latchman, Haniph ** (* Jamaica, ** United States)
ABSTRACT:
Power grids are made up of a robust collection of technologies that
have changed very little for decades. However in recent years the falling
costs of technology has led to rapid advancements the physical grid
and utility operations. This has given rise to novel solutions for problems
which have long plagued the energy sector such as the use of machine
learning, smart grids and the combination of the two to detect and mitigate
non-technical losses (NTL). NTL persist in developing countries where
much of the energy generated is used without payment, either willfully
through theft or unknowingly through meter defects. Developed countries
use their smart grid’s advanced metering infrastructure (AMI), coupled
with data analysis using machine learning to detect NTL. However, in
developing countries, such as Jamaica, where there is less smart meter
coverage, the utility cannot wait until all their meters are upgraded
to smart meters before it combats nontechnical losses. This paper quantifies
NTL in Jamaica, current trends in NTL detection and propose an effective
mitigation solution.
A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images
Attioui, Sanae; Najah, Said (Morocco)
ABSTRACT:
Facing the challenges of speckle noise and the difficulty of producing
labelled data in synthetic-aperture radar (SAR) image change detection
methods, we propose a novel change detection method by taking advantage
of a deep network. The main idea of the proposed method is to generate
the final change map directly from the two original images through a
Deep Belief Network (DBN) as the deep architecture without any preprocessing
operations, which prevents the process of generating the difference
image (DI), thus reducing the direct impact of the DI on change detection
performance. The training process of this network included unsupervised
feature learning followed by supervised network fine-tuning. Our two
main objectives are to reduce the impact of the presence of speckle
noise and also the processing time. On the one hand, high-quality training
samples were selected by introducing a fast and robust pre-classification
based on a morphological reconstruction and filtering of local members,
thus, avoiding the network to produce a lot of redundant functionalities,
and on the other hand, a virtual sample generation method that tries
to enrich the training samples used which results in a reduction of
overfitting raised by limited SAR data and a faster optimization of
the network. The experimental results on two real SAR image datasets
confirm the efficiency of the proposed method.
A Literature Review: Accommodating Kids in Inner City Developments
Malaila, Charles Pfungwa *; Burger, Michelle *; van Heerden, Andries (Hennie) **; Chawynski, Greg ** (* South Africa, ** New Zealand)
ABSTRACT:
The global concept of child friendly cities is important and therefor
needs to be explored. There is a growing need to analise the CBD and
ensure that it is a friendly environment for its inhabitants. This article
focusses on kids in the inner city developments. The importance of innovative
entrepreneurship for community good is apparent. Innovative development
is essential when planning and constructing within the inner city.
An Application of Event-Driven Platform for Smart City Decision Making
Saric, Andrej; Zakarija, Ivona; Batos, Vedran (Croatia)
ABSTRACT:
This paper reviews the smart city framework based on analysis of selected
event-driven models, presenting modified event-driven platform as the
part of implemented software solution. In addition, the outcomes open
future research opportunities. Rapid urbanization results in problems
such as lack of resources including energy and overcrowding. By applying
computing technology and the Internet of Things (IoT) we can prepare
models for the development of smart cities and reduce these problems.
In this paper, we propose a new event-driven platform triggered by smart
city events and by simplified data transformation leading to successful
decision making. The scope of input parameters includes tags, events
and probability factors, that are leveraged to prepare optimized decision
making process, and initiate reasonable actions.
Analysis of Risks to Data Privacy in All United Nations Member Countries
Patterson, Wayne (United States)
ABSTRACT:
Over 20 years ago, the surprising research by LaTanya Sweeney demonstrated
that publicly available database information exposed the overwhelming
percentage of United States residents to information easily available,
in order to facilitate the capture by hackers or other malevolent actors
of such personal information, through techniques we now refer to as
“dumpster diving.” In particular, her research demonstrated that approximately
87% of the United States population can be identified uniquely using
only the Unites States’ five-digit postal code, date of birth (including
year), and gender. Although this result has held up over time, Sweeney’s
technique made no attempt to develop similar estimates for other countries.
In this paper, we use Sweeney’s techniques in order to provide estimates
of the ability of similar demographics to provide the same type of data
for all United Nations member countries. Through this mechanism, we
attempt to determine the susceptibility to data privacy attacks throughout
a substantial portion of the world’s population.
Artificial Intelligence and Neuroscience: The Impact on Data Protection and Privacy
Fabiano, Nicola (Italy)
ABSTRACT:
Starting from a multidisciplinary approach, we want to investigate the
impact of high technologies used in neuroscience to analyse the effects
on data privacy and protection domain. It is still a field under a due
course of deepening, and probably there are few scientific pieces of
evidence, but it certainly is one of the most relevant challenges of
our times although some people think this is a topic of the future.
Neuroscience, data protection and privacy are current aspects, and we
should deal with them now to avoid unrecoverable consequences or distorted
findings. What will be the destiny of privacy and data protection in
the neuroscience domain? Our approach is not technical, and thus we
will not describe or propose specific technical solutions. Still, our
goal is to warn about the possible effects on data protection and privacy,
essentially on human dignity, hoping scientists would consider the principles
laid down by the current laws. In the neuroscience field, there is some
very innovative research on the human brain and behaviour where scientists
decided to use high-technologies to investigate the effects. Here comes
into play also another fundamental aspect: Ethics. We are facing a challenge,
and we already heard about "neuroprivacy". This new term entails examining
another privacy sector to deal with, and it led us to create a neologism
which we defined as "neuroprivacy rights". Hence, there is needing to
investigate all the legal effects on data protection and privacy derived
from applied technologies in the neuroscience field to clarify whether
we have a new category of rights. We think it is crucial to apply the
Data Protection and Privacy Relationships Model (its acronym is DAPPREMO)
in this deepening path.
Bestination: A Sustainable Approach to Problem Solving for Entrepreneurs
Sachayansrisakul, Navarat *; Ponnara, Nattawat ** (* Australia, ** Thailand)
ABSTRACT:
Despite our humanity has significantly progressed over the past decades
in terms of advanced technology, it has also brought along an excessive
consumerism and dysfunctional societies. People have the constant urge
to buy and consume unnecessary products and services just to be seen
on different social media platforms. This could be viewed as an opportunity
as well as a threat for the entrepreneurs. Our world appears to be smaller
in its size through better connection via social media platforms; and
yet people feel further isolated. By saying all this, this paper is
not against the advanced technology or capitalism. Rather, this paper
suggests a moderate approach to ‘moderation’ in business practices.
Bestination studies offer a moral and an ethical approach to achieving
the best destination for small to medium enterprises. Problem solving
is an essential component for entrepreneurs to deal with on a regular
basis. It is common for entrepreneurs to solve problems by dealing with
the end results without realising the actual causes of any problems.
Henceforth, this study provides a model to ethically solve problems
from the root causes so that it will lead to sustainability.
This research semi-structurally interviewed 93 entrepreneurs over the
period of 4 months. The respondents have operated their businesses beyond
their first few years of operation. The questions addressed are: How
can businesses identify root causes of a problem and solve it in a manner
that leads to sustainability? According to Buddhist principles
and some findings, a model called ‘The bestination problem solving model,
was developed for entrepreneurs.
Blockchain Technology and Cryptomarket: Vulnerabilities and Risk Assessment
Dumas, Jean-Guillaume; Jimenez-Garces, Sonia; Șoiman, Florentina (France)
ABSTRACT:
After ten years of continuous development and innovation, the cryptomarket
and the Blockchain technology are still very much challenged and far
from the mainstream adoption. We thus here propose a detailed risk assessment
based on a combined financial and technological analysis. We take into
consideration technological issues, such as consensus, network, cryptographic
primitives, quantum and smart contract attacks, together with financial
concerns such as market, information, liquidity, supply, reputation
and environmental risks. Then, to complete this study, we propose ways
to determine the probability that technological vulnerabilities can
trigger financial risk. Here, we tackle concepts such as financial behavior,
responsible investment and Blockchain literacy, as possible tools for
assessing risk. The results are relative to: 1. an identified continuity
between the technological risks and financial ones; 2. a way to determine
the likelihood of triggering financial risks through technical vulnerabilities.
Cognition Follows from Entropy Increasing and World Complexity
Mikheev, Yuriy (Russian Federation)
ABSTRACT:
The paper demonstrates that cognition is a direct result of functioning
fundamental physical phenomenon – the entropy increasing and the existence
of complex relations among observed events. Cognition is considered
as a process of changes in the system that results in getting the ability
to make accurate prognoses and decisions. Decision and in particular
accurate prognosis are the results of cognition functioning. In the
previous work [6] the Z measure was introduced and its usefulness for
regularities recognition was demonstrated. In this paper, we show the
connection of the Z measure with the second law of thermodynamics applied
to nonequilibrium dynamic systems [8]. On that basis, we infer that
cognition exists in such kind of systems and depends on the complexity
of relations among observed events of the environment. By several examples,
we show that the Z measure allows creating of artificial cognitive systems
that find out regular relations in various types of information - tabular
data, texts, images.
Constraint Programming as an AI Option
Abbott, Russ; Lim, Jung Soo (United States)
ABSTRACT:
We examine the history of Artificial Intelligence, from its audacious
beginnings to the current day. We argue that constraint programming
(a) is the rightful heir and modern-day descendent of that early work
and (b) offers a more stable and reliable platform for AI than deep
machine learning.
We offer a tutorial on constraint programming solvers that should be
accessible to most software developers. We show how constraint programming
works, how to implement constraint programming in Python, and how to
integrate a Python constraint-programming solver with other Python code.
Creative Accounting versus Fraud – An Interdisciplinary Approach
Chiriac (Matei), Alina; Nișulescu, Ileana (Romania)
ABSTRACT:
This article aims to present not only the similarities and differences
between creative accounting and tax fraud, but also the connection of
these two phenomena with the concept of underground economy. The topic
is one of real interest for both theorists and practitioners, due to
its controversial nature and the divergences of opinion in national
and international literature. The paper is of a qualitative type and
we used a series of bibliographic sources consisting of books, accounting,
tax and legal regulations, studies and articles published both nationally
and internationally by various bodies in the field, web pages of some
institutions with responsibilities in the field, both on national and
European Union level, in order to achieve the objectives set. The research
methodology begins with the identification of scientific databases that
host articles related to our research context. Ten major scientific
databases were selected. We have established four criteria, stipulating
that the article must (1) contain one of the keywords: tax fraud, tax
evasion, gray economy, underground economy, creative accounting or tax
optimization, (2) be written in English and / or Romanian, (3) have
been published between 1958 and 2020 or be approved for publication
(4) have the full text available in at least one of the ten databases.
Thus, approximately 62 years of research were reviewed. The main purpose
of this paper is to establish the delimitations of the terms. The results
reveal clear definitions of the concepts and the framing of each concept
in times of existing economy, but also the connection of each concept
with the other. Also, the implications of the results are that all actors
can outline an overview of the phenomenon, but especially reveals the
legislative gaps that need to be filled. The research is an interdisciplinary
one, because in order to understand the concepts we need many disciplines
such as ethics, law, taxation, accounting and more.
Development of Game-Based Learning Scenarios for Social Engineering and Security Risk Management for SMEs in the Manufacturing Industry
Scholl, Margit; Gube, Stefanie; Koppatz, Peter (Germany)
ABSTRACT:
With increasing digitization, information security (IS) is becoming
an important issue for all employees working in companies and organizations.
If the human factor is to be seen as strength rather than a weakness,
appropriate awareness-raising measures are required. One way to raise
awareness is through game-based learning (GBL), which can be used as
an ongoing means of motivating employees to engage emotionally with
the subject of IS and changing their online behavior accordingly. As
part of the project Mittelstand 4.0—Kompetenzzentrum Stuttgart (Mittel-stand
4.0—Competence Center Stuttgart), two analog GBL scenarios on the topics
Social Engineering and Security Risk Management for SMEs are currently
being developed over the period of a year, from April 2020 until March
2021. In this paper, the development process—including the phases prototyping,
testing, and adaptation—are described and the prototype results shown.
Testing analog prototypes in times of COVID-19 is particularly challenging.
The experience gained in this mini project will be incorporated into
the new three-year project Awareness Lab SMEs (ALARM) Information
Security, which is funded by the Federal Ministry of Economics
and has been running since October 1, 2020.
Distribution Based Image Classification and its Application in Person Re-Identification
Ding, Guangtai *; Chen, Fuhua **; Zhang, Xuemao ** (* China, ** United States)
ABSTRACT:
Image classification is widely used in many fields. Traditional metric
learning based classification methods always maximize inter-class distances
and minimize intra-class distances based on features calculated from
each individual. Different from traditional methods, this paper takes
each class as a distribution and try to maximize the distances among
different distributions using information geometry. In order to minimize
the distance among individuals inside a class, this paper assume that
each class follows a joint Gaussian distribution and take an exploratory
study on the relation between intra-class distance and the determinant
of the covariance matrix of the distribution. It is found that under
some assumptions, the average intra-class distance among the same class
is proportional to the standard deviation or product of standard deviations
of each feature. We therefore use the determinant of the covariance
matrix to substitute the intra-class distance in the metric learning.
The proposed method therefore saves a lot of computational cost. The
method is then applied to person re-identification. To our surprise,
the proposed method is very competitive than many state-of-the-art methods
while saving the computational cost in the learning process. Experimental
results demonstrate the effectiveness of the proposed method.
District Optimization Based on Population and Geometry – Using Taichung City as an Example
Huang, Hsiang; Tang, Cheng-Yuan; Hor, Maw-Kae (Taiwan)
ABSTRACT:
Districting has attracted the researchers’ interests recently. More
than ten years ago, the legislative election of the Republic of China
(ROC) has changed from elect multiple legislators in one district to
elect single legislator in one district. Hence, the districting method
has direct impact to the election outcomes. The Central Election Commission
(CEC) which in charge of the electoral affairs in ROC has established
a set rules for districting. CEC also announced the districting results
which are subjected to be reviewed every ten years. However, many of
the districts in the CEC’s newly announced districting results have
been found violate the population tolerance limits set by the CEC. Thus,
how to improve the existing districting results to reach a more fair
election has become the goal of this study.
In this paper, we proposed a mechanism to improve the existing districting
results using evolutionary algorithm through the knowledge of computational
geometry as well as the information obtained from geographic information
systems. One can seek for the fair districting results that satisfy
the districting regulations set by the CEC including the limits of population
error tolerance as well as the other issues. We also presented a set
of evaluation guidelines that can be used to evaluate the outcomes of
any districting methods. The population error, region contiguity, and
region compactness were all considered in our evaluation rules. Better
districting results can be obtained through our evolutionary algorithm
using acquire, release and exchanging operations.
The data of Taichung City was analyzed and used in testing our mechanism.
Based on the CEC’s regulations, Taichung City has to be districted into
eight districts. However, three districts in the districting results
announced by the CEC violate the population error tolerance limits set
by the CEC. Using our evolutionary mechanism, we have modified all the
districts of Taichung City that conform the districting rules successfully.
Domain Ontologies and the Conversion of Tacit Knowledge in Software Development
Evangelista, Euler; Muylder, Cristiana (Brazil)
ABSTRACT:
Purpose – This study presents a proposal to build and analyze
a domain ontology as a tool to support the knowledge transfer process
in the context of software requirements analysis in the medical/pharmaceutical
industry. The proposal is to use ontologies as an engineering artifact
with the objective of representing knowledge in a specific domain, which,
in the context of this research, is software modeling.
Design/methodology/approach – A domain ontology is built to represent
the requirements of a data warehouse/business intelligence software
in the medical/pharmaceutical industry. The ontology-building process
is supported by a specific methodology, defined with the purpose of
building such artifacts, named “Methondology,” and selected based on
the research requirements. A prototype is created in the implementation
phase of the ontology-building process.
Findings – The results demonstrate that ontology domains can
contribute to the process of analyzing and representing software requirements,
as well as serving as a tool for organizational knowledge transfer through
continuous knowledge conversion, which is critical for business sustainability.
Originality/value – This study is an attempt to understand the
knowledge conversion process in software development projects. Tacit
knowledge is complex to articulate through formal language once it has
been embedded with individual experience. Use of the artifact proposed
in this study can assist in externalizing the tacit knowledge needed
to elicit software requirements.
Economic Inequality and Power Imbalance in the United States: The Role of Globalization
Aboagye, Bright Da-Costa (United States)
ABSTRACT:
Social inequality has become a challenging social phenomenon in many
advanced countries. Individuals are affected by social divisions of
race, gender, economic, cultural, and political structures. Among these
social divisions, income and power inequality have become the major
political preoccupation in most developed countries. In the United States,
income disparity between the upper and middle classes has been increasing
for several decades. While the top 1% earners who contributed to 10%
of the U.S. national income in 1980 increased to 20% in 2016, the bottom
50% earners who contributed to 20% of national income in 1980 decreased
to 13% in 2016. There have been several interpretations of this phenomenon
but from a globalization point of view. This study, therefore, explores
the phenomenon of economic and power inequality from a globalization
standpoint. Using intersectionality as the theoretical framework, this
paper explores how various social constructs intersect in a globalized
economy to create income and power disparities. The author adopts a
systematic literature review approach to identify gaps, contradictions,
inconsistencies, interpretations, and connections in the literature
relative to the phenomenon being explored. The findings will add to
the scholarly literature on socioeconomic inequality and provide meaningful
recommendations to improve U.S. social policies.
Electromagnetic Security Vulnerabilities and Instruction Disassembly of Controller in Adaptive Controllers
Vaidyan, V.M.; Tyagi, Akhilesh (United States)
ABSTRACT:
A Controller in Adaptive control theory is a critical part in mission
critical applications in military and computer-controlled systems. An
ability to identify and follow the binary instruction execution in the
controller part enables fault identification and malware detection in
safety critical applications. Electromagnetic field emission based identification
of controllers execution state from distance will help ascertain security
vulnerabilities early on. Machine Learning models for instruction identification,
Principal Component Analysis (PCA), Adaptive Boosting (AB) and Naïve
Bayes (NB) were developed to meet this goal. Our preliminary results
of implementation on a 2-stage pipelined controller processor architecture
demonstrate that the EM side-channel classification approach identifies
a controller execution state in Adaptive control with 93% success rate.
Emerging Cyberbiosecurity Threats in the Chemical, Biological, Radiological, and Nuclear (CBRN) Domain
Franchina, Luisa; Inzerilli, Giulia; Scatto, Enrico; Calabrese, Alessandro; Coda, Natascia; Giuliana, Marco Antonio (Italy)
ABSTRACT:
Cyberbiosecurity is an emerging hybrid discipline built around the concepts
of cybersecurity and biosecurity. It deals with the inappropriate use
of valuable information, processes and materials pertaining to the areas
of life sciences and the digital world. The overwhelming industrial
and technological development, expanding globalization, permeability
of borders, along with the spread of terrorist movements at the international
level, are factors that amplify and fuel Chemical, Biological, Radiological
and Nuclear (CBRN) threats. In addition to the need for coordination
at the national and international level, there is the necessity to raise
awareness on the subject, among the public and the institutional world.
Considering that the nature of CBRN threat is transversal, we need a
truly multidisciplinary approach.
In this regard, establishing working groups composed of specialist technical
staff and intelligence analysts would be able to ensure the maximum
degree of coordination and support in the phases of preparation, prevention,
protection and response to the threat.
In an increasingly globalized world, the 2020 pandemic, which has caused
a high number of deaths and significant economic damage, has revealed
the vulnerability of Critical Infrastructure (CI) and health structures
of different countries. As a consequence of this critical situation,
a general interest in the danger of the spreading, both intentional
and unintentional, of biological agents increased due to the fact that
this type of threat could pose one of the most significant risks to
our societies in the future.
It would be desirable to build a strategic-institutional partnership
between the public and private sector aimed at including cyberbiosecurity
in the national architecture of each country system. This collaboration
would have a positive impact on the protection of corporate assets and
on the entire sector. Cyberbiosecurity and CBRN threats could affect
Critical Infrastructure. In order to tackle the severe problem represented
by internal threats to CI and all installations holding CBRN materials,
it is crucial to improve the exchange of information on nuclear - biological
- chemical - radiological materials, in relation to information on cyber
security and cyberbiosecurity.
Enterprise Systems and Threats
Blair, Risa (United States)
ABSTRACT:
The scenario included a medium-sized international company. The guidelines
were to select and include three enterprise systems that were based
on databases, one cloud-based and one that was not SQL-based. Systems
were accessible via a browser and included mobile applications. Of key
importance for this project was to research potential and known vulnerabilities
for these three enterprise systems. The systems selected were ADP Streamline
Payroll, Salesforce, and MongoDB. There are numerous threats described
in this project, including excessive privileges, SQLi attacks, weak
auditing, storage media exposure, unnecessary features enabled, broken
configurations, and buffer overflows. Enterprise systems are a potential
magnet for hackers on the black market and the Dark Web, as they provide
extensive confidential data, particularly in the technology, finance,
government, education, healthcare, and retail sectors. It was impressive
to see how both ADP and Salesforce provided up-to-date known and potential
vulnerabilities. What was the most interesting throughout the research
was uncovering the Mongo Lock ransomware and the Salesforce Meatpistol
malware. What is worse is that the Salesforce team provided a talk in
Las Vegas in July of 2017, where they explained how Salesforce attacked
its own system to see how well it would hold up against cyber attacks.
The talk focused on Meat pistol, a malware too for making it easier
to conduct the attacks from the standpoint of infrastructure automation,
implant creating, and interaction. The intent was to make it easier
for the Salesforce teams to conduct their attacks. They utilized the
methodology of the well-known tool, Metasploit, which does not exploit
systems or launch attacks. It just provides the framework for hackers
to control systems after they have been able to access what they choose.
The duo of “red team” inside hackers explained their process for access
the system through the utilization of Meatpistol, against the advice
of their superiors. Immediately after the presentation, they were fired.
Information Security: New Encryption and Decryption Methodology Based on e-Books Library Used in Plain Text Cryptography
Skrobanek, Paweł; Górski, Grzegorz; Wojsa, Mateusz (Poland)
ABSTRACT:
In this paper, a new methodology for the text encryption using e-books
library is proposed. The essential of the presented methodology is an
encryption based on the replacement of words from the encrypted text
into a vector of two numbers (in the basic version). These numbers determine
page (indirectly) and offset in a randomly selected book. In addition,
the algorithm, some results of experiments and examples of the application
of the presented method are given. The presented method can provide
additional protection to standard methods such as AES, DES or can be
used alternatively to them.
Interdisciplinary Skills Development Through Final Qualification Assessment: Survey Study for European and Oriental Languages Programs
Makhachashvili, Rusudan; Semenist, Ivan (Ukraine)
ABSTRACT:
The global pandemic and subsequent quarantine measures and restrictions
have posed an array of challenges to the structure and procedure of
university summative assessment process. Qualification assessment for
Foreign Languages major programs in particular is a strict regimen process
that involves different stages (oral and written exams, final project
viva, internal and external review). Cross-sectorial factors of societal
change, that provide the backdrop for an interdisciplinary skillset
critical transformation, crucial for the COVID-19 emergency educational
framework, are considered. The study premise is based on identification
of various interdisciplinary competency principles, derivative of 21st
century skills for university staff members and projected digital literacy
requirements. It is determined how in the situation of the COVID-19
pandemic lockdown all elements of the Final Qualification Assessment
at Borys Grinchenko Kyiv University for European and Oriental Languages
programs have been relegated to the digital, remote or blended format
with the use of ICT tools and skills that comprise an interdisciplinary
realm of Foreign Languages acquisition and assessment. Every step of
the procedure adaptation to digital format required accelerated development
of interdisciplinary skills of all participants and officials and cross-sectorial
activities, otherwise not carried out through assessment of Foreign
Languages programs.
Multivariate Analysis of the University Labor Climate in Virtual Emergency Education Conditions Due to the Coronavirus Pandemic
Villa González Del Pino, Eulalia M.; Pons Murguía, Ramón A.; Peñate Santana, Yaimara (Ecuador)
ABSTRACT:
The global situation experienced due to the advance of the covid-19
pandemic and the requirements of social isolation have affected multiple
sectors, especially the education sector. Teachers, support workers
and university students have taken on a great challenge by recognizing
that conditions have changed but learning is not delayed. Therefore,
they have seen the need to implement virtual education strategies in
a short period of time. For the universities in which face-to-face was
the daily learning model, which do not have the necessary infrastructure
for the new virtual modality, it has been a threat to the environment
as they were forced, under these conditions, to migrate from face-to-face
education to non-attendance. face-to-face, emergent way.
That is why this study emphasizes the adverse effects of the social
emergency caused by COVID-19 in the university teaching work environment,
the appearance of stress and the need for institutions to adopt measures
to improve their situation in terms of technology organization, methods,
techniques, and their digital skills in line with emerging global trends
and realities, to avoid negative consequences on the mental health of
their teachers and students.
Q-Learning Interacting with Kalman Filters
Takahata, Kei; Miura, Takao (Japan)
ABSTRACT:
Reinforcement Learning allows us to acquire knowledge without any training
data. However, for learning it takes time. We discuss a case in which
an agent receives a large negative reward. We assume that the reverse
action allows us to improve the current situation. In this work, we
propose a method to perform Reverse action by using Retrospective Kalman
Filter that estimates the state one step before. We show an experience
by a Hunter Prey problem. And discuss the usefulness of our proposed
method.
Security, Privacy and Interoperability Requirements for Peruvian Remote Digital Signatures
Papa Quiroz, Erik Alex; Cruzado Quispe, Ever; Quiroz Papa de Garcia, Rosalia (Peru)
ABSTRACT:
There are several technological solutions currently available in the
market that allow customers/citizens to digitally sign electronic documents
through their smartphones. Regardless of how user-friendly they are,
most of these platforms use proprietary schemes designed for particular
use cases, which could not necessarily be applied to open, interoperable
scenarios and where there could be legal consequences. To establish
a general framework that may provide manufacturers with minimum safety,
reliability, and legal requirements, several international organizations
have proposed standards for both manufacturers and potential users.
Within this context, this paper presents, for the first time in Peru,
a list of security, privacy, and interoperability requirements for remote
digital signature for the Peruvian state. This research was based on
features of the current state of the art, the existing international
standards and the current state of the technology. The relevance and
viability of these requirements were validated by RENIEC (Spanish acronym
of National Registry of Identification and Civil Status) specialist
personnel through an inter institutional cooperation agreement between
RENIEC and PNICP (Spanish acronym of National Program of Innovation
for the Competitivity and Productivity).
SOLVeR: A Blueprint for Collaborative Optimization in Practice
Blank, Julian; Deb, Kalyanmoy (United States)
ABSTRACT:
Collaboration among different stakeholders in achieving a problem-solving
task is increasingly recognized as a vital component of applied research
today. For instance, in various research areas in engineering, economics,
medicine, and society, optimization methods are used to find efficient
solutions. Such a problem-solving task involves at least two types of
collaborators – optimization experts and domain experts. Each collaborator
cannot solve a problem most efficiently and meaningfully alone, but
a systematic collaborative effort in utilizing each other’s expert knowledge
plays a critical and essential role. While many articles on the outcome
of such collaborations have been published, and the justification of
domain-specific information within an optimization has been established,
systematic approaches to collaborative optimization have not been proposed
yet. In this paper, methodical descriptions and challenges of collaborative
optimization in practice are provided, and a blueprint illustrating
the essential phases of the collaborative process is proposed. Moreover,
collaborative optimization is illustrated by case studies of previous
optimization projects with several industries. The study should encourage
and pave the way for optimization researchers and practitioners to come
together and embrace each other’s expertise to solve complex problems
of the twenty-first century.
Teaching Mathematics as Communication, Trigonometry Comes Before Geometry, and Probably Makes Every Other Boy an Excited Engineer
Tarp, Allan (Denmark)
ABSTRACT:
Before 1970 both foreign language and mathematics was hard to learn
because the two taught grammar before language. Then a turn took place
in foreign language education allowing students to learn it through
communication. Mathematics education never had a similar turn, so it
is still hard to many. Therefore, this paper asks if it is possible
to learn mathematics as communication. We see that three different kinds
of mathematics are taught, pre-setcentric, setcentric and post-setcentric.
Being inspired by the fact that children communicate about the physical
fact Many with two-dimensional box- and bundle-numbers with units, a
curriculum is designed where trigonometry is rooted in mutual recounting
of the three sides in a box halved by its diagonal. So, the answer is:
Yes, core mathematics can be learned as communication about boxes since
it is directly connected to counting and recounting Many in boxes and
bundles.
The Brotherhood and the Islamization Discourse in Egypt
Taha, Mohamed (United Kingdom)
ABSTRACT:
This paper focuses on changes in the Media-Political Communications
of the Muslim Brotherhood while in power in Egypt in 2012 and 2013.
The MB or al-Ikhwan al-Muslimun is regarded as the mother of Islamist
movements in the Middle East. During their period in power, the group
established its first TV channel Misr25 and launched a daily newspaper
al- Hurria wa al- 'Adala. No other studies have researched the communications
of the Brotherhood or their approach to media while they were in power.
The Brotherhood’s communications during this period were little more
than themes and trends that were communicated from the top down by the
group’s leadership to their media outlets, which lacked sufficient independence
to do their work based on editorial values alone. This study identifies
these themes, analyses them, and places them within the wider context
of the literature in historical and regional contexts. This paper concludes
that the Brotherhood’s main aim was to achieve a constitution with an
Islamic background regardless of hostility and criticism. The study
also shows that the Brotherhood moved towards antagonist discourses
as the opposition rallied against them, and underlines the troubled
relationship between the Brotherhood and the main actors in Egyptian
society, which were the army, the Christians and the secular opposition.
The paper uniquely answers questions related to the Brotherhood’s rule
in Egypt in 2012 and 2013 through the analysis of its media.
The Future of Education in Ghana: Promoting Critical Pedagogy Through Problem-Posing Education
Nkansah, Joan Nkansaa (United States)
ABSTRACT:
The instructional delivery methods in many Ghanaian tertiary institutions
are characterized by rigid curricula with little or no classroom discussions
and interaction. These practices restrict creativity and transformation
as students are separated from inquiry and only perform the role of
listening, memorizing, and repeating the thoughts and ideas teachers
narrate. Students lack exposure to learning environments that are conducive
to cultivate critical thinking skills and develop critical consciousness.
This qualitative case study explored how problem-posing education informs
the instructional delivery methods in a Ghanaian university. The study
focused on problem-posing education, a principle of Paulo Freire’s critical
pedagogy as the framework for the study. The study purposefully selected
11 participants (two faculty members, eight students, and one administrative
staff) who provided substantial data and deeper meaning and understanding
of the phenomenon. The data revealed that problem-posing education informs
the institution’s instructional delivery methods through problem-based
curricula content, entrepreneurial skill development, and feedback/partnership
opportunities. The study’s findings indicate that problem-posing education
advocates cognition and transformative learning.
The Interface of Human (Nous) and Artificial (Computer) Intelligence in Inter-Disciplinary Research, International Communication and Education
Nikolarea, Ekaterini (Greece)
ABSTRACT:
First, this study will be a philosophical/etymological
exploration into human intelligence (nous) and artificial intelligence
(computer / computare), and a SWOT analysis that their interface
offers to all scientists and, especially, to those whose English is
not their mother tongue. Second, it will be a meta-cognitive
discussion about whether non-English scientists know: (1) about the
existence of computer tools – such as electronic dictionaries, CAT [Computer
Assisted Translation] tools and how to use them; and (2) what to do
when they “hit” on issues of inter-scientificity (e.g. “bar” with 17
different terms in Greek) and reverse inter-scientificity (e.g. πρόγραμμα
with 6 different terms in English). Third, it will
discuss that only human mind/intelligence (nous)
- with the aid artificial intelligence (computer –CAT tools) and through
different mental/cognitive processes (noesis) - can establish
certain criteria in choosing appropriate terms and expressions, so that
an inter-disciplinary research can be communicated properly. Finally,
it will propose that HEIs in North America and in Europe should, first,
become “aware” (nous - noesis) of the concepts of
inter-scientificity and reverse inter-scientificity and, then, train
their administrative and academic staff in those concepts and how to
use translation tools appropriately, should they wish to achieve an
appropriate and an effective international scientific communication.
Unsupervised Machine Learning Applied to Multivariate Time Series Data of a Rotating Machine from an Oil and Gas Platform
Figueirêdo, Ilan Sousa; Carvalho, Tássio Farias; Silva, Wenisten Dantas da; Guarieiro, Lílian Lefol Nani; Santos, Alex Alisson Bandeira; Filho, Leonildes Soares De Melo; Vargas, Ricardo Emmanuel Vaz; Nascimento, Erick Giovani Sperandio (Brazil)
ABSTRACT:
Deep Learning (DP) models have been successfully applied to detect and
predict failures in rotating machines. However, these models are often
based on the supervised learning paradigm and require annotated data
with operational status labels (e.g. normal or failure). Furthermore,
machine measurement data is not commonly labeled by industry because
of the manual and specialized effort that they require. In situations
where labels are nonexistent or cannot be developed, unsupervised machine
learning has been successfully applied for pattern recognition in large
and multivariate datasets. Thus, instead of experts labeling a large
amount of structured and/or non-structured data instances (also referred
to as Big Data), unsupervised learning allows the annotation of the
dataset from the few underlying interesting patterns detected. Therefore,
we evaluate the performance of six unsupervised learning algorithms
for the identification of anomalous patterns from a turbogenerator installed
and operating in an oil and gas platform. The algorithms were C-AMDATS,
Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest.
The evaluation performance was calculated with seven classification
metrics. The C-AMDATS algorithm was able to effectively and better detect
the anomalous patterns, and it presented an accuracy of 99%, which leverages
the further development of supervised models.
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