The College of Computing and Software Engineering (CCSE) Tutoring Lab welcomes all
undergraduate and graduate students enrolled in CGDD, CS, CSE, CYBR, IT, and SWE courses!
We have progressed to an appointment-based tutoring system using the EAB Navigate.
To schedule an appointment, please view below. If you need help when no tutor is available,
please see the Program Coordinator in the Main Lab Center for assistance.
Tutoring Policies & Procedures
The Tutoring Lab is for students who are currently enrolled in the course.
For undergraduate students :To schedule an appointment please use the link under “Tutoring” section. Your appointment can be “In-person”, “Online”, or come in Tutoring Lab as walk-in for all courses available for tutoring.
Please make an appointment in advance. You can schedule up to two appointments per
day for each course you currently enroll in. Each session is 30 minutes.
If you cannot attend the scheduled session, be sure to cancel your appointment at
least 2 hours before the session. Your account will still be open.
If you no show and miss three appointments, the system automatically locks your account and you will be blocked from making additional tutoring appointments for the remainder of the semester
For graduate students:You can come to the Tutoring Lab as a walk-in or connect with Program coordinator to schedule “In-person” or “Online” appointment. Graduate students can have help with courses with Languages as Java, C++, C#. For all other courses please contact your instructor.
Courses Available for Tutoring
The courses we tutor include the following listed below. Please note that all subjects/languages
do not have tutors available at all times. When booking an appointment, click on the
"calendar day" to see the next available appointment time.
CSE 1321: Programming and Problem Solving I
CSE 1321L: Programming and Problem Solving I Laboratory (Java, Python, C#, C++)
CSE 1322: Programming and Problem Solving II
CSE 1322L: Programming and Problem Solving II Laboratory (Java, C#)
CSE 3153 : Database Systems
CS 3305: Data Structures
CS 3410 : Intro to Database Systems
CS 3502: Operating Systems
CS 3503: Computer Organization and Architecture
CS 3503L: Computer Organization and Architecture Lab (Assembly, Digital Trainer)
CS 4306 : Algorithm Analysis
CS 5000: Foundations of Programming
CS 5020: Computer Architectures and Operating Systems
After logging into Navigate, you should see a box that says Upcoming Appointmentson the right of your screen. Click on the appointment link by selecting the word(s) in blue.
In this example, I would click on C#.
Select where it saysCanceland then select theReasondrop-down menu.
In the drop-down menu, select the reason you are canceling. For example, you could
select“Emergency”or“Sick.”You may put additional details in theComments box
When you have filled out all the fields, selectMark as CanceledorCancel Appointment
Once you have confirmed the appointment is canceled, you may click onClose. The appointment should disappear fromUpcoming Appointmentsand you may also get an email.
The Main Lab is an open and collaborative-friendly environment that is available to all KSU students and is equipped with computer desktops, tables with power outlets, paper shredder, printer, etc.
Location: Marietta Campus, Atrium, J Building – Room 263
The CCSE Virtual Desktop Infrastructure (VDI) environment is virtualization technology
that hosts a desktop operating system on a centralized server in our data center.
CCSE offers two types of VDI environments: persistent and nonpersistent. Persistent
VDI (assigned based on class) provides each user with his or her own desktop image,
which can be customized and saved for future use, much like a traditional physical
desktop. Nonpersistent VDI (CCSE Standard) provides a pool of uniform desktops that
users can access when needed. Nonpersistent desktops revert to their original state
each time the user logs out.
The goal of the College of Computer Science and Software Engineering Data Research Center (CCSE-DRC) is to provide a cloud service infrastructure for big data analyses and deep learning using cutting-edge software platforms. The CCSE-DRC provides different services based on the needs of our faculty and students.
CCSE-DRC architecture consists in physical machines used as sole resource and mainly
as cluster. Our cluster lays the foundation for complex software frameworks.
VM’s
KVM’s machines with any Linux distribution are available. Standard setup include 2 Cores, 8 GB memory, and 100 GB Storage, but it can expanded as previous requirement to the CCSE-DRC. Those machines could be Desktop or servers, and can be access using CLI or GUI (SPICE- VNC). For GUI access the user should install the GUI client.
Version
Compiled against library: libvirt 6.0.0
Using library: libvirt 6.0.0
Using API: QEMU 6.0.0
Running hypervisor: QEMU 4.2.1
LXD Containers
LXD is a next generation system container manager. It offers a user ex- perience similar to virtual machines but using Linux containers instead. Some of the advantages of containers over VM’s includes quicker spin- ning up apps, and better share of resources which is vital in our academic community. Similar to VM’s we offer CLI and Desktop access for our users, and deployment of multiples containers to create a personalized micro-cluster.
Version:
Lxd version: 4.0.7
Deep Learning Servers
These are shared resources used by our GRA’s students and faculties. Each server has 4 Tesla Maxwell Nvidia’s GPU’s per server and an ISCSI storage server attached.
The main deep learning frameworks are installed such as PyTorch, Ten- sorflow, Keras, and Caffe. These servers also include the most important Python libraries for machine learning and data science like Numpy, Pan- das, SciPy, Matplotlib, SymPy, Sckits-image, Sckits-learn, Pandas, Bokeh, and Jupyter among others. In addition, R-cran statistical language, Julia mathematical language and Unix tools are installed.
The user can install their own Python libraries using virtual environ- ment, and R packages as no administrators. In case that the user needs administrative rights to install system libraries the servers provides Stan- dard Docker and Docker-nvidia containers. Please ask to CCSE-DRC for more information.
Versions:
torch 1.9.0
torchsummary 1.5.1
torchvision 0.10.0
tensorboard 2.6.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0
tensorflow 2.5.0
jupyter 1.0.0
jupyter-client 6.1.12
keras 2.6.0
keras-nightly 2.5.0.dev2021032900
Keras-Preprocessing 1.1.2
matplotlib 3.4.2
matplotlib-inline 0.1.2
numba 0.53.1
numpy 1.19.5
opencv-python 4.5.3.56
pandas 1.3.1
Hadoop Framework
Hadoop is an open-source software framework used for storing and pro- cessing Big Data in a distributed manner on large clusters of commodity hardware.Apache HDFS or Hadoop Distributed File System is a block- structured file system where each file is divided into blocks of a pre- determined size. These blocks are stored across a cluster of one or several machines. Apache Hadoop HDFS Architecture follows a Head/Worker Architecture, where a cluster comprises of a single NameNode (Head node) and all the other nodes are DataNodes (Worker nodes). HDFS can be deployed on a broad spectrum of machines that support Java. Though one can run several DataNodes on a single machine, but in the practical world, these DataNodes are spread across various machines. CCSE-DCR provides a cluster with 10 nodes at the moment but can be expanded as needed. This is a shared resource and can be used to experiment with new map-reduced algorithms or to process huge data.
Versions:
In Production
Hadoop 2.7
Spark 2.1
Very Soon:
Spark Standalone Cluster 3.1.2
In Progress:
Hadoop 3.0
Spark 3.1.2
Streaming Framework
Classic data analysis was performed over data resting mostly on a rela- tional database or plain text. The natural consequence of this approach is that the analysis may be conducted on the whole collected data, or on a data batch accumulated over a period of time. Subsequently, because of the massification of portable devices, traditional storage methods be- came insufficient due to the enormous amount of fluid data available. Thus, new technologies like streaming analytics emerged to solve pre- vious limitations.
Streaming analytics processes never ending data originated from con- nected devices(IoT), people networks (social media), and interrelated complex systems (autonomous platforms) among others. Some goals of streaming analytics are to facilitate real time statistical analysis, to per- form machine learning analysis and training, and to interact with other frameworks for permanent data storage. Real time analysis refers not only to real time data analysis, but also to the analysis of data batches
collected over short periods of time, ranging from seconds to minutes. In addition, these systems must be able to place the information in tem- porary storage during a specific period of time and should also be able to store more than one temporal batch.
CCSE-DCR provides a cluster with 2 Kafka nodes at the moment but can be expanded as needed. This is a shared resource for more information contact CCSE-DRC.
Version:
Kafka: 0.10
Kubernetes
Kubernetes, at its basic level, is a system for running and coordinating containerized applications across a cluster of machines. It is a platform designed to completely manage the life cycle of containerized applica- tions and services using methods that provide predictability, scalabil- ity, and high availability. In conjunction with Kubernetes we use Helm which is a package manager for Kubernetes that allows developers and operators to more easily package, configure, and deploy applications and services onto Kubernetes clusters. For more information contact CCSE- DRC
Version:
kubectl: 1.21.3
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.
Azure Development Tools for Teaching
Faculty, Staff and Students can sign in with their netIDs and passwords and then go
to the software section to see everything. This is only for Microsoft products but
it includes operating systems and other development software including Project and
Vision.
Access to NETLAB+ is fast and easy, requiring only a web browser with a Java plugin.
Build-in applications for virtual machine and device console are included. NETLAB+
provides scheduled access to your virtual machines and lab equipment. All lab access
is controlled by reservation, using the scheduler. Using the calendar interface, students
and instructors can view the pods and the timeslots available to schedule lab time
at their convenience.