Category: Computer science

MUST ANSWER ALL DISCUSSIONS QUESTIONS. I HAVE PASTED THE INFO FROM CHAPTER 3 BUT CAN GIVE YOU LOG IN INFO FOR THE BOOK IF NEEDED
WHAT TO SUBMIT:
Respond to the case study questions below related to the Uber case study. Your submission should be 2 to 3 pages, double spaced, and submitted as a Word document. Also, 2 to 3 resources are required and must be appropriately cited using APA style. These resources can include the textbooks and resources from previous modules, as well as original resources you have consulted. Please write out the questions in your document.
Your responses should be in complete paragraphs and should contain the following:
Answer all of the questions thoroughly and completely. Write out the questions in your document.
Make direct connections between the issues identified in the case study and the concepts covered in the provided resources in Modules One and Two.
Support your answers with appropriate examples and facts drawn from the case study.
Use correct grammar, sentence structure, and spelling, and demonstrate an understanding of audience and purpose.
Discussion Questions:
1-Considering Uber’s mission statement and business model, in what ways do the mission and business model align with the decision to use TMC?
2-Uber is faced with the monumental challenge of managing and motivating millions of drivers who are important to its business, but who aren’t full-time employees. How effective do you think Uber’s “automated manager” is as a managerial control system for Uber drivers? Please explain.
3-What are the benefits to Uber of using TMC through its mobile app? What are the downsides?
4-What impact, if any, do you think Uber’s use of TMC has on its organizational culture? How does the fact that most of Uber’s employees are remote contractors influence the culture?
5-
How might differences in national cultures influence the response to TMC? How might Uber change or modify TMC to make it more effective?
Overview
In this module’s readings, you learned about how information technology interacts with and influences organizational structure and work design. Chapter 3 of Managing and Using Information Systems discusses how “Management control at the individual level is concerned with monitoring (i.e., data collection), evaluating, providing feedback, compensating, and rewarding. It is the job of the firm’s leaders to ensure that the proper control mechanisms are in place, and the interactions between the organization and the IS do not undermine the managerial objectives and worker performance” (p. 75).
In this activity, you will read about how Uber is using technology to monitor its drivers’ behaviors. The use of this technology is known as technology-mediated control (TMC), which refers to an organization’s use of digital technologies to influence workers to behave in a way that is consistent with its strategic and tactical objectives.
For this week’s activity:
Read the case study below, adapted from the Chapter 3 case study, “Uber’s Use of Technology‐Mediated Control.”
Consider ways in which TMC influences organization structure, culture, and work design.
Respond to the provided discussion questions below.
Prompt
Uber Technologies is a ride‐hailing company that uses the cars and time of millions of drivers who are independent contractors. The company operates in many countries around the world. One recent estimate is that Uber drivers globally spend 8.5 million hours on the road every day. Uber’s mission statement is “Transportation as reliable as running water, everywhere for everyone.” Uber also employs a dual strategy that aims to deliver value to drivers and riders alike by appealing to each group’s different incentives by creating a mutually beneficial relationship between the two.
Uber wants to control how these drivers behave and exerts this control not through human supervisors, but through a system of algorithms that serves as an automated virtual manager. Drivers’ work experiences are entirely mediated through Uber’s mobile app. Hence, Uber has been accused of using TMC to exert “soft control” over its drivers.
This app is constantly collecting data on drivers. It nudges the behavior of the drivers in such a way that in reality they aren’t as much their own boss as they might like to be. For example, while they can work when they want, Uber’s surge fare structure of charging riders more during high‐volume periods motivates drivers to work during times that they might not want to work. The app even sends push notifications based on sophisticated algorithms. For example, “Are you sure you want to go offline? Demand is very high in your area. Make more money, don’t stop now!”
The mobile app also employs interventions to encourage various driver behaviors. For example, the Uber app will let drivers know that they are close to achieving an income target when they attempt to log off and end their work. Uber also sends drivers their next fare opportunity before their current ride is over. New drivers are enticed with signing bonuses when they meet initial ride targets.
To motivate drivers to complete enough rides to earn bonuses, they occasionally receive words of encouragement through the app. The app also watches their rides to ensure that they accept a minimum percentage of ride requests, are available for a minimum period of time in order to qualify to earn profitable hourly rates during specified periods, and complete a minimum number of trips.
How is all this monitoring influencing Uber’s drivers? Uber’s turnover for drivers is high—reportedly closing in on 50% within the first year.
CASE STUDY FROM BOOK
Case Study 3‐1 Uber’s Use of Technology‐Mediated Control
Uber Technologies, founded in 2009, is a ride‐hailing company that leverages the cars and time of millions of drivers who are independent contractors in countries around the globe. One recent estimate by Uber Group Manager, Yuhki Yamashita, is that Uber drivers globally spend 8.5 million hours on the road—daily. As independent contractors, Uber tells its drivers “you can be your own boss” and set your own hours. Yet, Uber wants to control how they behave. Uber exerts this control not through human managers, but through a “ride‐hail platform on a system of algorithms that serves as a virtual ‘automated manager.’” Drivers’ work experiences are entirely mediated through a mobile app.
Uber’s mobile app collects data and guides the behavior of the drivers in such a way that in reality they aren’t as much their own boss as they might like to be. For example, while they can work when they want, Uber’s surge fare structure of charging riders more during high‐volume periods motivates them to work during times that they might not otherwise choose. The app even sends algorithmically derived push notifications like: “Are you sure you want to go offline? Demand is very high in your area. Make more money, don’t stop now!” Hence, Uber uses technology to exert “soft control” over its drivers.
Uber employs a host of social scientists and data scientists to devise ways to encourage the drivers to work longer and harder, even when it isn’t financially beneficial for them to do so. Using its mobile app, it has experimented with video game techniques, graphics and badges and other noncash rewards of little monetary value. The mobile app employs psychologically influenced interventions to encourage various driver behaviors. For example, the mobile app will alert drivers that they are close to achieving an algorithmically generated income target when they try to log off. Like Netflix does when it automatically loads the next program in order to encourage binge‐watching, Uber sends drivers their next fare opportunity before their current ride is over. New drivers are enticed with signing bonuses when they meet initial ride targets (e.g., completing 25 rides). To motivate drivers to complete enough rides to earn bonuses, the app periodically sends them words of encouragement (“You’re almost halfway there, congratulations!”). The mobile app also monitors their rides to ensure that they accept a minimum percentage of ride requests, complete a minimum number of trips, and are available for a minimum period of time in order to qualify to earn profitable hourly rates during specified periods. Uber has a blind acceptance rate policy, where drivers do not get information about the destination and pay rate for calls until after they accept them. This can mean that drivers might end up accepting rates that are unprofitable for them. On the other hand, drivers risk being “deactivated” (i.e., be suspended or removed permanently from the system) should they cancel unprofitable fares. The system keeps track of the routes taken to ensure that the driver selected the most efficient route.
The mobile app also captures passenger ratings of the driver on a scale of one to five stars. Since the drivers don’t have human managers per se, the passenger satisfaction ratings serve as their most significant performance metric, along with various “excellent‐service” and “great‐conversation” badges. But how satisfied are the drivers themselves? Uber’s driver turnover rate is high—reportedly closing in on 50% within the first year that the drivers sign up. One senior Uber official said: “We’ve underinvested in the driver experience. We are now re‐examining everything we do in order to rebuild that love.”
Discussion Questions
Uber is faced with the monumental challenge of controlling and motivating millions of drivers who are important to its business, but who aren’t on its payroll. How effective do you think Uber’s “automated manager” is as a managerial control system for Uber drivers? Please explain.
What are the benefits to Uber of using technology‐mediated control through its mobile app? What are the downsides?
What impact, if any, do you think Uber’s use of technology‐mediated control has on its organizational culture?
Do you think the Uber digital business model is a sustainable one? Please provide a rationale for your response.
Sources: JC, “How Many Uber Drivers Are There?” Ridester, January 29, 2019, https://www.ridester.com/how‐many‐uber‐drivers‐are‐there/ (accessed February 18, 2019); Wiener and Cram AMCIS 2017 and Cram and Wiener 2019 Communications of the Association for Information Systems (forthcoming); IBID and N. Scheiber, “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,” New York Times, 2017, https://www.nytimes.com/interactive/2017/04/02/technology/uber‐drivers‐psychological‐tricks.html (accessed February 18, 2019); and A. Rosenblat, Uberland: How Algorithms Are Rewriting the Rules of Work (Oakland, CA: University of California Press, 2018).

For this assignment, you will complete the following hands-on activity using Office 365™ Word. You will be creating a Holiday newsletter. This will allow you to demonstrate that you have learned how to insert images and use headers/footers. Be sure you review the Resources provided this week if you are not sure how to accomplish these things.
Attached above are 4 pictures and 3 documents to help you with the assignment.
Follow the step-by-step instructions: NewsletterInstructions2.pdf.
An unformatted document containing the stories that you can modify: NewsletterStories2.docx.
A final Holiday newsletter (that you cannot modify) so you can see what your document should look like when you are done: NewsletterSolution2.pdf.
Pictures: Family2.jpg, house2.jpg, ocean-reef.jpg, majestic dolphin.jpg
Make any additional adjustments so your newsletter fits on one page (the content and images) and the name, assignment, and class information added is on page 2 and resembles the solution. To fit everything for the newsletter on one page your images may need to be altered down in size to help everything fit.
Use the instructor’s feedback provided with your previous assignment grades to improve your work as needed
Attach your assignment as a Word (*.docx) document by clicking “Browse Local Files” or “Browse Cloud Service” and locating the document you wish to attach. You do not need to use the Text Submission. Comments are optional.
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I will provide articles once I confirm that I am going to have written from you. I want to publish this as review paper in high impact factor journal. 
Outline for Survey Journal Article on Small Object
Detection using Deep Learning (font size 12 and 1 line spacing include images
and tables, 100+ references)
I. Introduction (>20 references)
Overview
of Object Detection in Computer Vision
General
introduction to object detection.
Historical
context and evolution of object detection techniques.
Importance
of Small Object Detection
Applications
requiring small object detection (e.g., medical imaging, autonomous
vehicles, aerial imagery).
Challenges
and significance in these applications.
Deep
Learning in Object Detection
Introduction
to deep learning and its role in revolutionizing object detection tasks.
Key
advantages of deep learning over traditional methods.
Purpose
and Structure of the Article
Aim
of the survey: Comprehensive review of deep learning approaches for small
object detection.
Outline
of the paper’s structure.
II. Challenges in Small Object Detection (>25
references)
Unique
Challenges
Scale
variance, low resolution, occlusion, background clutter, and lighting
variations.
Detailed
discussion on how these factors affect detection performance.
Impact
on Deep Learning Models
How
the above challenges specifically hinder deep learning models.
Example
scenarios illustrating these challenges.
III. Deep Learning Architectures for Small Object
Detection (>50 references)
Foundational
Architectures
Introduction
to Convolutional Neural Networks (CNNs).
Review
of foundational architectures like AlexNet, VGG, and ResNet.
Specialized
Architectures
Detailed
review of architectures tailored for small object detection (YOLO, SSD,
Faster R-CNN, etc.).
Modifications
and adaptations for small object detection.
Strengths
and weaknesses of each architecture.
Recent
Advancements
Attention
mechanisms, feature fusion techniques, and other state-of-the-art
developments.
Case
studies and comparative analysis.
IV. Training Strategies for Small Object Detection (>25
references)
Importance
of Training Data
Role
of training data quality and quantity in model performance.
Challenges
in acquiring and preparing training data (limited labeled datasets, class
imbalance).
Data
Augmentation Techniques
Techniques
like random cropping, flipping, scaling, and their effectiveness.
Case
studies demonstrating improvements with data augmentation.
Advanced
Training Strategies
Transfer
learning, domain adaptation, and semi-supervised learning.
Techniques
to address class imbalance and enhance model robustness.
V. Evaluation Metrics for Small Object Detection (>15
references)
Common
Evaluation Metrics
Average
Precision (AP), Mean Average Precision (mAP), and other traditional
metrics.
How
these metrics are calculated and interpreted.
Limitations
and Recent Advancements
Limitations
of traditional metrics for small object detection.
Introduction
to new metrics designed for small object detection.
VI. Applications of Small Object Detection using Deep
Learning (>25 references)
Medical
Imaging
Detecting
tumors, microcalcifications, and other anomalies in medical images.
Case
studies and current advancements.
Autonomous
Vehicles
Detection
of pedestrians, cyclists, traffic signs, and other small objects.
Real-world
implementations and challenges.
Remote
Sensing and Aerial Imagery
Identifying
vehicles, buildings, and natural features from satellite or aerial
images.
Case
studies and specific applications.
Other
Applications
Surveillance,
wildlife monitoring, etc.
Specific
examples and impact analysis.
VII. Open Challenges and Future Directions (>15
references)
Current
Challenges
Computational
efficiency, real-time performance, robustness to diverse environments.
Detailed
discussion on unresolved issues.
Future
Research Directions
Potential
advancements in deep learning architectures.
New
training data acquisition methods and evaluation metrics.
Integration
with other technologies (edge computing, IoT).
VIII. Conclusion
Summary
of Key Findings
Recap
of the significant developments in small object detection using deep
learning.
Overall
impact and importance.
Future
Outlook
Potential
future directions and research opportunities.
Final
thoughts on the evolution of the field.
IX. References ()

Prompt: After reading about the surge of internet computer crimes, discuss the following: How have computer crimes driven changes in information technology security? How does the Fourth Amendment apply to computer crimes?

Referring to the case file “evidence-malware.pcap” in the CH12-Malware folder . Answer the following questions.
1. What operating system and browser version were in use by Vick at the time of the attack? 2. Vick’s initial HTTP request is missing a Referer header. What could this indicate about the source of the attack?
3. What was the full URL (including port) of the GIF file that was requested by the victim’s browser?
4. Which of the following was the delivery mechanism for the Internet Explorer exploit?
(A) An backdoored GIF file
(B) Javascriipt code
(C) A Windows executable
(D) An infected PHP module
5. What was the timestamp of the initial port 4444 connection (initial SYN packet)?
6. What type of file was downloaded via the port 4444 connection? What was the likely purpose of this file?
7. The Windows executable file that was downloaded contains a function that calls for an HTTP GET request. What is the contents of this GET request?
8. What is the MD5 checksum of the malicious Windows executable file?
9. Vick’s computer makes several failed connection attempts to the malicious server on port 4445. Approximately how often does the source port change during these connection attempts?
10. What is the CVE number of the Internet Explorer exploit used in this attack?

There are many situations when you need to email your professor: Asking a question, inquiring about your grades, informing them about a missed class, etc. If you’re wondering how to write an email to a professor, in this week’s assignment you will be able to practice doing just that.
You will not need to send an actual email. Instead, upload and attach your Word document file to Blackboard.
To fill in the “To” line, go to the Instructor Info tab in the Blackboard course menu and find the professor’s email address. Then, type that into the “To” line.
Write a clear Subject line. There are many situations when you might need to email your professor: Asking a question, inquiring about your grades, informing them about a missed class. Try to use a real scenario from this course.
Include a proper email greeting. Start your email to a professor with an appropriate and respectful salutation. Double-check their name before sending an email and make sure your greeting is followed by a comma.
Remind the professor who you are. Professors have lots of students, so it’s important to tell them your name and the class you’re attending. This helps the recipient save time and ensures you get a reply faster.
Get straight to the point. After greeting a professor and introducing yourself, it’s time to state your question or request. Keep it concise and clear, so the recipient can quickly comprehend what it’s about and what action is expected from them.
End an email politely and include a professional signature. Thank them for their time and sign off your email with “Sincerely” or “Best regards” followed by your name.
Proofread your email. Pay attention to grammar, spelling, and punctuation. Make sure to stick to a formal tone and avoid emojis or informal abbreviations like FYI or ASAP. Check the spelling of your professor’s name one more time.
Put yourself in your professor’s shoes. Reread the email as if you are the professor who receives it. Is it clear who’s writing to you and what they want? Is the tone of the email polite and respectful? Does it comply with a formal email format? If all your answers are “Yes,” then feel free to send your email.
communicating with instructors template_2237.docx 

Showing initiative in the workplace is a prime reason why people receive promotions, get raises, and, in the case of interns, receive offers of full-time employment. In a 2-3 page APA-style paper, address the following. Support your work as appropriate with citations and references. Describe 2-3 actions that you have taken in your internship workplace that demonstrated your initiative. Have you offered to do something above and beyond your normal duties? Have you figured out a better way to do a common task? How did your supervisor or coworkers respond to your effort? If you have difficulty identifying ways in which you have shown initiative, you may instead describe the performance of a co-worker or supervisor in your workplace who is a good example of a person who shows initiative and puts forth extra effort. Identify some extra ways you might do your job more efficiently. These suggestions could be tasks that require higher-level skills than your job description requires.

Review the ISO standards and certification options for businesses using the links provided in this week’s readings. Write a proposal for a business (preferably your current organization) to seek ISO 27002:2022 certification. Provide business justification and develop an initial implementation plan. Answer questions such as what will be covered in the certification, policies to be written, and training to be provided within the organization.

Introduction
Writing a high-quality manuscript in computer science requires attention to detail, clarity, and adherence to established conventions. Whether you’re working on a research paper, conference paper, or journal article, the following guidelines can help you produce a manuscript that is clear, well-structured, and effectively communicates your research.
Ø Title:
Choose a concise and informative title that clearly reflects the content of your paper.
Avoid vague or overly general titles.
Ø Abstract:
Write a clear and concise abstract that summarizes the main contributions, methodology, and results of your research.
Include key terms and concepts to make your paper easily discoverable.
Ø Introduction:
Clearly state the problem or research question your paper addresses.
Provide context for your work and highlight the significance of your contributions.
Clearly state your research objectives and hypotheses.
Ø Related Work:
Provide a comprehensive review of related work in the field.
Highlight the gaps in existing research that your work aims to address.
Cite relevant and recent sources.
Ø Methodology:
Clearly describe the methods and techniques used in your research.
Provide sufficient detail to allow others to reproduce your experiments.
Discuss any limitations or assumptions in your methodology.
Ø Results:
Present your results in a clear and organized manner.
Use tables, figures, and graphs to enhance the presentation of data.
Provide statistical analysis where applicable.
Ø Discussion:
Interpret your results and relate them to your research objectives.
Discuss the implications of your findings and how they contribute to the field.
Address any limitations and suggest areas for future research.
Ø Conclusion:
Summarize the key contributions and findings of your research.
Clearly state the practical and theoretical implications of your work.
Ø References:
Follow a consistent citation style (e.g., APA, IEEE, ACM) throughout your manuscript.
Ensure that all cited works are included in the reference list and vice versa.
Ø Clarity and Style:
Write in clear, concise, and grammatically correct language.
Use a consistent writing style and avoid unnecessary jargon.
Ensure a logical flow of ideas between sections.
Ø Figures and Tables:
Ensure that all figures and tables are labelled and referenced correctly in the text.
Provide clear and informative captions for each figure and table.
Ø Peer Review:
Seek feedback from colleagues or mentors before submitting your manuscript.
Revise and edit your manuscript based on the feedback received.
Ø Formatting:
Follow the formatting guidelines of the target journal or conference.
Pay attention to font, margins, spacing, and other formatting details.
By following these guidelines, you can enhance the quality and impact of your manuscript in computer science. Remember to tailor your writing to the specific requirements of the venue where you plan to submit your work.
Conclusion
In conclusion, crafting a high-quality manuscript in computer science demands meticulous attention to detail and adherence to established guidelines. A well-structured paper begins with a clear title and concise abstract, leading to a thorough introduction that outlines the problem statement and research objectives. A comprehensive review of related work sets the stage for a robust methodology, results presentation, and insightful discussion. The conclusion should succinctly summarize key contributions and their implications while maintaining clarity and adherence to the chosen citation style. Through meticulous formatting, thoughtful organization, and peer feedback, researchers can ensure their work not only meets academic standards but also makes a meaningful impact in the field of computer science.

Initial Post Instructions
This week you learned how to create professional presentations. It is not only important to know how to use all of the features in PowerPoint, but also when it is appropriate or not to apply certain options. Let’s start talking about what you consider to be best practices when creating a PowerPoint presentation. Also, describe which features you considered the most helpful versus those that could be distracting or challenging to use. Finally, share how you are planning to convince the board to approve your research funding( see attached presentation) Share how you setup your presentation to convince them.
Follow-Up Post Instructions
Respond to at least one peer(see link). 
https://cdnapisec.kaltura.com/index.php/extwidget/preview/partner_id/2363221/uiconf_id/43522921/entry_id/1_c76vdipu/embed/dynamic
Further the dialogue by providing more information and clarification. This is a hands-on course and it is expected for you to use more visuals such as screenshots and video. Make sure to utilize that since it is a requirement.