|A Case Study: Incorporating Parallel and Distributed Computing into Computer Science Curriculum|
Ali Abu-El Humos, Sungbum Hong, Tzusheng Pei, Omar Aljawfi
Recent technology advances in parallel computing such as multicore CPUs, GPUs, and their driving software require a well-prepared workforce to support this demanding and fast changing industry. Parallel and Distributed Computing (PDC) education for computer science and computer engineering majors will play a major role in preparing well trained graduates to join this workforce. In this work, we share past and future plans to update the computer science curriculum at Jackson State University (JSU) with PDC modules. As part of this effort, some of the NSF/IEEE-TCPP curriculum initiative on PDC modules were integrated into department-wide core and elective courses offered in both fall and spring semesters. These courses were: CSC 119 Object Oriented Programming (core) [2, 4, 6, 9], CSC 216 Computer Architecture and Organization (core) [3, 5, 9], CSC 312 Advanced Computer Architecture (core) [3,5], CSC 325 Operating Systems (core) [6, 9], CSC 350 Organization of Programming Languages (core) , CSC 425 Parallel Computing (elective) [1, 2, 6] , CSC 499 Special Topics: Data Mining (elective) and UNIV 100 University Success course, which is a university-wide class offered for all JSU majors. In an effort to update the contents of the UNIV 100 course, some contemporary PDC topics and their essence in higher education were incorporated into this course. The inclusion of the PDC modules was gradual and light weighted in the lower level courses and
more aggressive in the higher-level courses to let the students easily grasp PDC concepts. Specific test questions, homework assignments and projects were developed to assess students’ performance.
Mathematics Education with Conjectures and Refutations
In this paper we discuss how Popper’s epistemology and
Lakatos’ view on the development of mathematics may provide
some interesting ideas to mathematics educators in both primary
and secondary education. We present a teaching strategy which
can complement more traditional approaches and we illustrate it
with an example.
Understanding College Student Perceptions of Artificial Intelligence
Artificial intelligence (AI) is such a part our lives it seems to be changing us, or at least how we do life, without us being aware of its gradual omnipresence. Despite the push by business and government to develop AI, there is a concern about the real effects it is having on society. This study was designed to explore student perceptions of AI through quantitative statistical methods. The results of this study suggest that the general perception of AI is positive, but there exists some concern about the rapid development of AI and how it will affect humankind. In particular, being more informed about AI developments has a significant influence on both positive and negative perceptions of its impact on individuals and society. Furthermore, this paradox, along with the lack of understanding about AI, seems to add to a social tension arising from the inevitable advance of technology and the uncertainty of the effect of AI.
Consulting via Research in IMPRESS
Patrick Ködding, Jannik Reinhold, Michel Scholtysik, Roman Dumitrescu
Manufacturing companies in mechanical and plant engineering are increasingly aiming for the change from product manufacturer to digital service provider. Digitalization is bringing data-driven digital services – so-called Smart Services – into focus. However, the resulting systems of hybrid value creation and work differ fundamentally from the systems established today.
Especially small- and medium-sized companies face major challenges concerning the transformation to a Smart Service provider. The reason for this are the historically grown corporate structures of value creation and work. Additionally, there is often a lack of sufficient expertise and resources to identify and implement necessary changes. Many small and medium-sized enterprises are aware of the potential of Smart Services. But the realization and offering of Smart Services has so far only taken place occasionally. Therefore, we cannot but deduce that a comprehensive socio-technical consulting approach is required to launch Smart Services efficiently.
The paper at hand presents an approach which addresses these challenges holistically by using research-based consulting. It is based on the Design Research Methodology according to Blessing and Chakrabarti. The adapted consulting via research approach is applied and validated by four case studies from tooling machine industry within the joint research project IMPRESS.
Interactive Effect of Information Systems and Instructional Systems Design on Digital Leadership Training Development
Elspeth McKay, Rozinah Jamaludin
This paper explores the relationship between information systems (IS) and instructional systems design (ISD) to innovate an extended Baldridge Performance Indicator (BPI) model for developing massive open online courses (MOOCs). The project is part of an ongoing collaborative research project between the Universiti Sains Malaysia, Penang and RMIT University, Melbourne, Australia, to design and implement online digital leadership training modules for the Malaysian higher education leadership academy (AKEPT). To date, the education sector has been slow to adopt the BPI for academic performance recognition. This paper presents a work in progress that highlights the importance of designing effective training ePedagogies, which promote good digital leadership skills based on four MOOCs modules: vision, mission and values; digital workforce environment; digital workforce engagement; and ethical digital leadership behaviour. The extended BPI model will enable accurate measurement of visionary academic digital leadership, such as: (1) definition and application of key knowledge and concepts; (2) independence, self-determination, self-teaching and self-motivation; (3) problem-solving, thinking and computing skills; (4) strong ethical characteristics and values; (5) awareness of societal, health, safety, legal and cultural issues; and (6) recognition of the need to undertake life-long learning.
Toward a Comprehensive Smart Ecosystem Ontology – Smart Cities, Smart Buildings, Smart Life
Dagobert Soergel, Renata Maria Abrantes Baracho, Matthew T. Mullarkey
This paper posits a smart ecosystem as a complex system with
several interdependent components or subsystems: Natural
environment, smart city, smart buildings, smart office, smart
manufacturing, smart life. Understanding, design, and operation
of such a system can be supported by a comprehensive ontology.
We introduce the structure of ontologies as consisting of a
schema-level ontology, and entity-value-level ontologies, for
each entity type a taxonomy of entity values. One can
developing a comprehensive ontology by collecting and
integrating specifications from many sources; we illustrate this
process by building a very preliminary taxonomy of (smart)
ecosystem functions from seven sources. Making ecosystems
smart can improve the quality of life and contribute to more
|Real-Time, On-Site, Machine Learning Identification Methodology of Intrinsic Human Cancers Based on Infra-Red Spectral Analysis – Clinical Results|
Yaniv Cohen, Arkadi Zilberman, Ben Zion Dekel, Evgenii Krouk
In this work we present a real-time (RT), on-site, machine-learning based methodology for identifying intrinsic human cancers. The presented approach is reliable, effective, cost-effective and non-invasive and based on the Fourier transform infrared (FTIR) spectroscopy - a vibrational method with the ability to detect changes as a result of molecular vibration bonds using infrared (IR) radiation in human tissues and cells.
Medical IR optical system (IROS) is a table-top device for real-time tissue diagnosis that utilizes FTIR spectroscopy and the attenuated total reflectance (ATR) principle to accurately diagnose the tissue. The ATR measurement principle is performed utilizing a radiation source and a Fourier transform (FT) spectrometer. Information acquired and analyzed in accordance with this method provides accurate details of biochemical composition and pathologic condition of the tissue.
The combined device and method were used for RT diagnosis and characterization of normal and pathological tissues ex-vivo/ in-vitro. Therefore, the presented device can be used in close conjunction with a surgical procedure
The solution methodology is to select a set of "features" that can be used to differentiate between cancer, normal and other pathologies using an appropriate classifier. These features serve as spectral signatures (intensity levels) at specific values of measured FTIR-ATR spectral responses.
Excellent results were achieved by applying the following three machine learning (ML) based classification methods to 76 wet samples: Partial least square regression (PLSR) and Principal component regression (PCR)
Both of the methods (PCR & PLSR) show a high performance to classify "Cancer" or "non-Cancer"; Correct Classification: 100 %; Incorrect Classification: 0.0 %.
Naive Bayesian classifier (NBC); Shows a high performance to classify "Cancer" or "non-Cancer" (benign); Correct Classification: 100 %; Incorrect Classification: 0.0 %.
Information Warfare: Memes and their Attack on the Most Valued Critical Infrastructure—the Democratic Institution Itself
Marc Dupuis, Andrew Williams
Social media has become a potent vector for the spread of disinformation. Content initially posted by bots, trolls, or malicious actors is often picked up and magnified by ordinary users, greatly extending its influence and reach. In order to combat disinformation online, it is important to understand how users interact with and spread this type of content, unwittingly or not. We studied patterns in the sharing of propaganda and disinformation on social media through political image-based memes. Initially, we began with a selection of 12 memes. In our first survey, we narrowed this down from 12 memes to six based on the responses received with respect to the message it was trying to convey and representation of varying viewpoints along the ideological spectrum. Ultimately, we chose a selection of six memes. Four of them involved climate-change with two considered left-leaning and the other two right-leaning, and the remaining two were focused on particular politicians and also split along ideological lines. Next, we conducted a second survey in order to better understand the behavior of ordinary users as they interact with propaganda and disinformation on social media. Particular attention was paid to differences based on political affiliation and psychological factors, including personality and trait affect. Negative types of affect appear to dominate the level of engagement Republicans and Independents have with memes, while positive types of affect and extraversion do the same for Democrats.
Research and Consulting in Data-Driven Strategic Product Planning
Maurice Meyer, Maximilian Frank, Melina Massmann, Roman Dumitrescu
Industry 4.0 and digitalization have transformed the industrial world. Many manufacturers create additional customer value by offering data-based services. However, companies can benefit from analyzing data themselves, too. Through data, companies can learn about product usage and behavior. This enables them to systematically improve their products. But finding improvements through data analysis is not trivial.
Henceforth, we developed a method for the data-based iden-tification of product improvements. This method was created in a joint research project with four companies from different industrial sectors.
The paper at hand introduces our approach of combining research and consulting in terms of a case study from our research project. The result is a research and consulting concept which is optimized for a two days workshop. From our point of view, there is no other way in researching methods for strategic product planning but through working together closely with companies. This is especially important as methods must be researched for practical usage. Simultaneously, it is essential to never forget that companies only participate in research projects if they clearly see a benefit. A benefit through consulting.
Orthogonal Megatrend Intersections: "Coils" of a Stellar Transformer
N. Christian Smoot, Bruce Leybourne
According to the plate tectonic hypothesis, fracture zones (FZs)
are considered transform faults that lie perpendicular to midocean
ridge axes; that is, they show the direction of seafloor
spreading. Bathymetric maps of the Pacific Ocean basin exhibit
a multitude of latitudinally trending FZs as well as
longitudinally trending FZs on the Pacific plate. By the early
1980s the FZs were found to be active features with magma
leakage along trend, which shifted the conception that linear
seamount chains must form as hot spot traces. With the
inclusion of seamount chains into the FZ trends, coupled with
near multi-beam total coverage bathymetry and 1st order
Geodetic Earth Orbiting Satellite (GEOSAT) structural trends
the concept of intersecting megatrends evolved. Analysis
reveals that oceanic rises and plateaus generally sit atop the
intersections of these FZs, exhibiting continental blocks, large
igneous outpourings, and/or tectonic vortex structures at the
intersections. Additionally, these megatrends are shown to
continue into the continents, such as the Murray and Mendocino
FZs in the northeastern Pacific, intersecting and crossing, the
San Andreas Fault trend in California. The intersecting
megatrends exhibit magnetic anomaly patterns related to
magmatic intrusive/extrusive events not necessarily
corresponding to seafloor foundation of Archean (original
lithosphere) crust 4 – 2.5 billion years ago. Nor can the plate be
spreading in several directions at the same time. Evidence of
orthogonally intersecting megatrends coupled with a dubious
interpretation of seafloor magnetic lineation age hypothesis
leads investigators toward a more robust explanation of tectonic
events. By understanding plasma tectonics is driven by space
weather in which these orthogonal FZs act as “coils” of a stellar
transformer, a new paradigm emerges linking solar induction
and space weather as drivers of seismic and volcanic energies
such as earthquakes and magma production.
Analysis of Cyclic Deformation of Erythrocyte in Couette Type of Pulsatile Shear Field
Shigehiro Hashimoto, Ryo Muto
The cyclic deformation of an erythrocyte has been measured microscopically in the pulsatile shear field to detect dynamic deformability of an erythrocyte in vitro. A rheoscope system has been manufactured to observe deformation of suspended erythrocytes in the shear flow. The rheoscope consists of a pair of parallel disks and an inverted phase-contrast microscope. The human erythrocytes were suspended in the dextran aqueous solution, which has high viscosity. Erythrocytes are sheared in the Couette flow between the pair of counter rotating disks. The rotating speed varies sinusoidally to make the pulsatile shear field. Deformation of each erythrocyte was measured at the video image of the rheoscope. The experimental results show that the system is available to measure the following behavior of an erythrocyte. The ellipsoidal shape of each erythrocyte varies cyclically to follow the pulsatile cyclic shear field. The phase of deformation of each erythrocyte in the cycle delays from the sinusoidal fluctuation of the shear field according to its own dynamic deformability.
An Optimal Deep Learning Approach for Classification of Age Groups in Social Network
Anil Kumar Swain, Bunil Kumar Balabantaray, Jitendra Kumar Rout, Suneeta Satpathy
There is huge amount of data in social networks, where people
post their opinion on a topic, or share their information. But
people often don’t provide their personal data, like gender, age
and other demographics. Research can be done on this data to
develop applications of sentiment analysis, but the success rate
is restricted by the number of words in the dictionaries as they
do not consider all the words which reflect the sentiment in our
messages as most of the communication on social networks is
non-standard language with small messages. Moreover, with
contemporary technology it is quite easy to create profile with
false age, gender and location which provides criminals an easy
way to deceive. Thus we can analyze the text messages posted
by the user on social network platform. As per the research
done so far, age is one of the important parameter in the user
profile which reveals the important information about the
typical behavior among same age group users. An analysis is
done with more than 4000 tuples which contains relevant
parameters like number of friends, length of message, number
of likes, number of hash tags and comments are considered for
the classification. In this study, we use the user profile
information for the prediction of age group, which we collected
using Facebook API. In this paper we classified the users into
two age groups teenagers and adults using different Machine
learning algorithms like deep convolutional neural networks,
Multilayer perceptron, Random forest , SVM and Decision trees.
Among all these algorithms deep convolutional neural network
stands out to be the best among all of them reaching the best
performance with an accuracy of 94%.