ENG2575 Technical Writing, Section OL70, Fall 2020

TO: Professor Ellis

FROM: Jinquan Tan

DATE: 9/20/2020

SUBJECT: 500-Word Summary Draft

This memo is a 500-word summary of the article, “Remote interpreter API model for supporting computer programming adaptive learning,” by Yuana, R. A., Leonardo, I. A., & Budiyanto, C. W.

Software engineering  at this time is very necessary, the preparation of skilled human resources is essential. Efforts that can be done is to develop effective learning methods, and adaptive learning is one of them. Adaptive learning technologies provide an environment that can intelligently adapt to the needs of individual learners through the presentation of appropriate information, comprehensible instructional materials, scaffolding, feedback, and recommendations based on participant characteristics and on specific situations or conditions. Adaptive learning can consist of several characteristics, namely: analytics, local, dispositional, macro, and micro. Yuana said, “computer programming learning is indispensable for many exercises and needs extra supervision from the teacher”(Yuana,2019,p.154). Students that have difficulty in making program algorithms can be solved. a teacher can guide students to learn programming by monitoring them. There are many adaptive learning models for programming learning. E-learning facilitated students’ psychomotor ability requires a capability that enables students to write program code directly into and evaluated by a particular module in the electronic learning. The adaptive learning concept to improve students psychomotor ability during online learning/teaching using commercial off-the-shelf LMS. The psychomotor interaction between students and LMS will be demonstrated by the use of adaptive learning in computer programming courses. The Proposed Model Works: “The transactions processes that occur in the web API model started from LMS server. In the LMS, the user writes program code using a code editor. Subsequently, a POST method sends the program code, together with the input-output value, and also the function name of the program code to an API caller”(Yuana,2019,p.154). How JSON structure of web API response : “ code file for later use by the interpreter along with standard input using pipe technique. Once the interpreter executes the program code the output is read by the API module”(Yuana,2019,p.155).Research method Remote interpreter web API model. : The first step is to create a web API model and the second step is to Testing the performance of the web API model. The developed web API model can be implemented with system topology by letting few clients connect to one LMS Server and then let the LMS Server connect to Web API Server. Clients need to run the program code created by them in order to work. Web API model Performance Analysis : “Once the web API model is implemented it is ready for a performance test. The test scenario was to send the program code containing a large number of looping using Python and PHP from client to web API server”(Yuana,2019,p.158).

In conclusion, web API model that serves to run the interpreter based-program code has been developed. It can support computer programming adaptive learning. Both input and response structure has been adapted to suit students’ psychomotor ability assessment in learning program code writing. It is evident that the web API module demonstrated its performance during the test.

References:

Yuana, R. A., Leonardo, I. A., & Budiyanto, C. W. (2019). Remote interpreter API model for supporting computer programming adaptive learning. Telkomnika, 17(1), 153–160. https://doi.org/10.12928/TELKOMNIKA.v17i1.11585

TO: Professor Ellis

FROM: Adewale R. Adeyemi

DATE: 09/14/2020

SUBJECT: 500-word summary draft

“This is a memo for my 500-word summary of the article “Security and Privacy in the Medical Internet of Things”

Medical internet of things (MIoT) is a group of devices that can connect to the internet and monitor patient vital signs through wearable and implantable devices. It has been an efficient new technology for the healthcare system. It’s made up of the perception layer which collects vital data through wearables, the network layer which transmits the data collected the perception layer and the application layer which provides the interface needed by the users and also integrates the information from the other two layers.

As MIoT is been made use of extensively by more patients, security and privacy of these patient’s data cannot be taken for granted. This is also paramount to its success. Due to the amount of real-time data MIoT transmits, it is important to provide enough resources to protect patient’s security and privacy. Below is the 4 security and privacy recommendation. Data integrity, usability, auditing, and patient information are all recommendations that deal with how patient sensitive data is access and stored. Most MIoT devices have very low memory and the data that has been collected needs to be stored. cloud storage is currently been used and it as some existing solution to security and privacy requirements. Encryption: through cryptography is implemented at three levels of communication, link, node, and end-to-end encryption. Node is the most secure of the three because it does not all data transmission on plain text in the network node. Securing patient data is important but less complex algorithm needs to be utilized to reduce resources usage and have a fast transmission rate. A key transfer managed has been proposed to help tackle this problem. Authors claim, “To secure e-health communications, key management protocols play a vital role in the security process.” (Sun, Cai, Li, Liu, Wang, Fang, 2018, P 3). A lightweight key management that is strong and uses less resources is being used while a lightweight algorithms and encryption based on the problems the healthcare system is facing is being improved upon. Access control is another solution that authenticates users based on set policies to authenticate the user trying access sensitive data and it is important because patient data are shared electronically. Third party auditing is another solution. Since patient’s data are stored in the cloud, the service provider needs to be audited to know if their practices are ethical. Data anonymization is another solution which consists of sensitive patient data and identifier. K-anonymity is current being used it has it flaws which is being improved on. As technology advances, future security, and privacy challenges in MIoT will arise. Among them is insecure network (WIFI) which can be vulnerable to man in the middle attack, lightweight protocols for devices and data sharing. MIoT is still improving and more successful proposition will still be made.

References

            Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Security and Privacy in the Medical Internet of Things: A Review. Security & Communication Networks, 1–9. https://doi.org /10.1155/2018/5978636

TO: Professor Ellis

FROM: Nakeita Clarke

DATE: Sept 20, 2020

SUBJECT: 500-Word Summary

This memo is a 500-word summary of the article, “Should Artificial Intelligence Be Regulated?” by Amitai Etzioni, and Oren Etzioni.

Anxiety regarding Artificial Intelligence (AI) and its potentially dangerous abilities have surfaced the question of whether or not AI should be regulated. A key component, and a first step to approach such regulation would involve standardizing a universally objective definition of AI. Some predict that it is inevitable for AI to reach the point of technological singularity and believe it will happen by 2030. This perspective is due to AI being the first emerging technology with the capability for producing intelligent technology itself, which is interpreted as a foundational threat to human existence. Respected scholars and tech leaders agree AI possesses such a threat and urge for the governance of AI. The Association for the Advancement of Artificial Intelligence (AAAI) suggests that there is no foreseeable reason to pause AI-related research while the decision to monitor AI is being determined. Others see no reason for regulation stating, “machines equipped with AI, however smart they may become, have no goals or motivations of their own.” (Etzioni, A., & Etzioni, O., 2017, p. 33). Even so, it may already be too late to attempt to create international regulations for AI due to global widespread usage across public and private sectors.

Both sides agree on the social and economic impact AI will cause; however, regulation could inflate the cost of such an impact. So far, AI has exhibited superior medical advantage, sped up search and rescue missions leading to increased chances of recovering victims, and is used in the psychological industry for effective patient care. AI is already used in our everyday technology from personal assistants; Google Assistant, Alexa, Siri, and Cortana, as well as security surveillance systems. Instead of regulating AI as a whole, limiting the progression of its beneficial impact, focusing AI regulation on AI-enabled weaponry may be a more actionable approach. Public interest in doing so exists and is evident from petitions urging the United Nations to ban weaponized AI. Existing treaty on Nuclear weapons could be an indicator that countries across the globe may adopt one for AI. In addition to such a treaty, a tiered decision-making guidance system could aid the management of AI systems. On the flip-side, what about the management of AI-powered defense, de-escalation and rescue machines in combat zones?

AI’s disruption of the job market has begun and will create an unevenness causing additional unemployment and income disparities. Despite job loss, economists believe AI will lead to the creation of new types of jobs. Having a committee to monitor AI’s impact, as well as advise on ways to combat job loss due to AI-based initiatives could mitigate social and economic threats AI presents. One can be hopeful that an almost utopian alternative to AI’s negative impact is possible if society changes its response to AI, starting with public open dialogue as the driving force for productive policies.

References

ETZIONI, A., & ETZIONI, O. (2017). Should artificial intelligence be regulated? Issues in Science & Technology, 33(4), 32–36.

TO: Professor J Ellis 

FROM: Brianna Persaud 

DATE: 9/19/2020

SUBJECT: 500- Word Summary  

This is a 500- word summary of the “Peer Support and STEM Success for One African American Female Engineer” by Shari Earnest Watkins and Felicia Moore Mansah, of The Journal of Negro Education. 

African Americans face hardships that other races typically don’t have to when pursuing a career related to STEM. “A handful of researchers have investigated the experiences of African American PhD Scientists and have found race to be an influential factor for persistence in their STEM careers (Brown et al., 2013; Pearson.” In the article that was assigned to my class and I, there were several studies conducted to identify these obstacles that African-Amercians, particularly African-Amercian women face. These studies were conducted by Dr. Jenkins in an effort to fight for the betterment of her race and equal opportunity. Dr. Jenkins studied as an undergraduate at an HBCU and pursued a Master’s degree immediately afterwards at graduate school. 

According to Dr. Jenkins, studying as an undergraduate was one of the best experiences of her life. Her coming to this conclusion was influenced by establishing peer relationships within her HBCU. Dr. Jenkins believes that peer relationships ultimately have the most influence on African-American women studying under STEM programs. Dr. Jenkins also goes on to state that establishing peer relationships (with same race peers in particular) assisted in building confidence, passion and companionship. She also credits much of her success to her peers that she established relationships with during her time as an undergraduate due to how close she became with them. Along with her peers, she dedicated a lot of time to studying as well. While Dr. Jenkins emphasizes the importance of establishing peer relationships in college as an African American, she also discusses how racism and race  affects those of her descent  that are particularly not in the same environment as she was during her undergraduate studies. Studies along with Dr. Jenkins Graduate school experience indicates that racism plays a significant role in determining whether African American students succeed in pursuing their degree under the STEM umbrella. Oher students that aren’t placed in that same environment as her are automatically at a disadvantage due to lack of fair treatment and equal opportunities.

As Dr. Jenkins began to talk about her experience as a graduate student, she talks about the unexpected struggle she began to face while not being in the same environment as she was as an undergraduate. Dr. Jenkins was no longer surrounded by the same peers she was before, making it incredibly hard on herself to stay motivated and to achieve the same status she once had academically as well. In her new environment, Dr. Jenkins felt isolated due to her new peers not being willing to assist her while also excluding her from a lot of experiences. Dr. Jenkins even states that during her graduate studies, her peers were very ‘cliquish’ and tended to stay within their own race. If it wasn’t for her same race peers outside of her university, ‘superstar jaheed’ in particular, she believes that she wouldn’t have been able to achieve her Masters degree.  All in all, as an African American student, racism is very prevalent in education, so comradery can alleviate that hardship while also guiding you to achieving your STEM degree. 

Reference: 

       Watkins SE, Mensah FM. Peer Support and STEM Success for One African American     Female Engineer. Journal of Negro Education. 2019;88(2):181-193. doi:10.7709/jnegroeducation.88.2.0181

TO: Professor Jason W. Ellis

FROM: Tasnuba Anika

DATE: Sept 18, 2020

SUBJECT: 500-Word Summary

            Artificial intelligence (AI) is one of the fields of the computer science that highlights the fabrication of intelligent machine that performs different tasks and acts like humans. There are many artificial intelligence methods like fuzzy expert systems, Bayesian networks, artificial neural networks, and hybrid intelligent systems are playing significant role in health care.

            AI in medicine are divided into two categories such as virtual and physical. The virtual section deals with securing patient’s medical history into an electronic database or providing neural network-based recommendation to the physicians. The neural networks on computer process information and can suggest solution to the critical problems like human brain. Using robots for surgeries, intelligent prostheses for disable people are the physical usage of AI.

           Computers can identify patient’s health problems in two different ways. One is flowchart style and the other one is database. In the flowchart style doctors must provide the common syndromes and diseases that patients usually experience into machine-based cloud networks. Afterwards when the physician put the syndromes in the machine that a patient is facing, it shows what kind of disease that patient has. However, this approach of diagnosing patient’s sickness is not very useful. Sometimes the machine cannot spot the illness. On the other hand, database technique has proven to be very functional. As it deploys the object identification systems. In this system computers are fed thousands of clinical or radiological images, different types of diseases and symptoms. In this way computers can recognize patient’s illness with 75% accuracy.

          The usage of AI is very broad in medical field. AI has been utilizing in online scheduling, digitalization of medical records, prescribing medicine dosage, immunization dates for children and pregnant women and so on. Computer-assisted diagnosis (CAD) is getting popular in assessing mammography. In CAD process it indicates whether the screening has been done appropriately or not. It also points out negative tests in X-rays or MRIs specially in hospitals which have fewer staffs. Moreover, there is a system called Germwatcher which spots and examine germs and infections. With help of an online course called AI-therapy now patients can treat social anxiety. Besides that, doctors are now using robotic arms that can imitate human hand movement and provides 3D view with zooming options. Robotic arms help surgeons to perform precise surgical cuts. In addition, health trackers like Fitbit and apple track human activities like heart rate, sleeping and so on. In this way doctors could get better insight of patients’ health.

         The use AI is rapidly increasing for which physicians and medical trainees should get themselves familiarized with the modern technologies and get trained properly. As the author in this article mentioned “The goal should be to strike a delicate mutually beneficial balance between effective use of automation and AI and the human strengths and judgment of trained primary care physicians.” This is basic since AI totally supplanting people in the field of medication is a worry which may somehow, or another hamper the advantages which can be gotten from it. Machines can never perform critical thinking, creativity, or emotional intelligence like human. That is why patients would still need doctors to treat their disease.

                                                           References

Amisha, Malik, P., Pathania, M., & Rathaur, V. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine & Primary Care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19

TO: Professor Jason W. Ellis

FROM: Mahim M. Pritom

DATE: Sept 17, 2020

SUBJECT: 500-Word Summary

This memo is a 500-word summary of the article, “Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values,” by Choirul Huda, Herman Tolle & Fitri Utaminingrum, researchers at Brawijaya University, Malang, Indonesia.  

The article discusses the facial recognition system capable of identifying closed eyes and generating results of the Eye Aspect Ratio(EAR) by analyzing Facial Landmark points with an accuracy rate of about 92.85%.

According to the studies, sleepiness while driving causes injuries from minor to life-threatening that results in death in both developed and developing countries. Even though automotive industries started to develop special devices designed to keep drivers awake while driving, only certain brands of vehicles such as Mercedes-Benz, BMW, Volvo have the technology leaving the people driving a vehicle equipped with simple technology still at risk of sleepiness driving-related injuries due to the expensive production cost.

Rahman et al. conducted a study to identify sleepiness based on eye blink analysis by using a webcam connected to a computer. He used the Viola-Jones method to detect the eye area of the face using the Haar-like feature algorithm that helps to expose the sleepy eyes and evaluating the frame of the closed-eyes caught on camera. Jacobe et al. conducted another study utilizing two models of Artificial Neural Network(ANN) to predict sleepiness in a driving simulation. Even though both studies gave satisfying results, there were problems such as converting the color of each frame, inadequate detection process, and obstacles to install the camera inside the car. Mohammad et al. utilized the Haar Cascade Classifier by using mobile to detect and observe the eye sclera both closed and opened situation to determine the color reflecting back. Although this method can generate high accuracy result, there are some limitations such as the driver’s face must be aimed directly towards the camera, needs further development to recognize eye areas precisely. Another method was proposed by Jabbar et al. to detect drowsiness by combining Deep Neural Networks and extraction of facial points. Although this system could identify the drowsy drivers by 81%, the application process of this method requires a computer with high specifications because of the complex learning process which is very expensive and time-consuming.

Soukupova and Cech used the EAR method to measure the facial landmark in the eye area using Euclidean Distance that helps to recognize the blinks using 6 different landmarks. Since the number of extraction points applied in this study was limited, the most influential points in closed eye detection are selected to speed up the computing process. The authors, on these limited extraction points, emphasizes, “The Facial Landmark method qualified to identify driver sleepiness accurately based on analysis of EAR (Eye Aspect Ratio) values utilizes the extraction of 4 points around the eye area” (Huda et al., 2020, p. 27). The tree Regression method is applied to complete the precision procedure of incompletely processed points that are produced during the extraction process. The library is used to determine the number of dots generated on the face. Face detection is utilized as the library to detect the extracted facial landmark dots using a face detector developed by Google. Soukupova and Cech assigned a threshold value of 0.20 compared with the EAR resulting in opened eyes if EAR>0.20 or closed eyes if EAR<0.20.

Reference

Huda, C., Tolle, H., & Utaminingrum, F. (2020). Mobile-Based Driver Sleepiness Detection Using Facial Landmarks and Analysis of EAR Values. International Journal of Interactive Mobile Technologies, 14(14), 16–30. https://doi.org/10.3991/ijim.v14i14.14105