ABSTRACT:
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) [9], 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.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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 sustainable communities.

ABSTRACT:
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 %.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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.

ABSTRACT:
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%.