Online Computer Lab Security

In Chapter 1 and Chapter 2, we have done overviews about the issues related to online computer labs such as Web-based teaching and various Web-based teaching systems. We have also briefly discussed technology-based courses and the technologies that can be used in the development of WBT and online computer labs. Starting from this chapter, we will discuss issues in designing and developing online computer labs for technology-based courses. The first task in designing online computer labs is to determine what type of online computer lab should be developed. It all depends on the teaching requirements. The developed computer lab should meet the needs of hands-on practice and balance the support, cost, and the complexity of technologies. This chapter provides you with a systematic way of identifying the needs of an online computer lab. In this chapter, we will discuss various approaches to decide what the teaching requirements are. We will start off with the topics related to online computer lab development process which will show you the big picture about developing online computer labs. Then, we will walk through the topics such as identifying hands-on requirements, identifying resources, and assessing costs. Investigating these topics will help you decide how big your project is, what the growth rate is, what the costs are, if there is funding for the project, and what kind of support you need to get from the computer service team. The next topic is about project planning which deals with issues such as budgeting, scheduling a time line, forming a project development team, and implementing the project. This topic helps designers to deliver an efficient plan in developing a successful online computer lab.

A mobile ad hoc network (MANET) is a dynamic wireless network which can be formed without the need for any pre-existing infrastructure in which each node can act as a router. In comparison with fixed networks, some new security issues have arisen with the introduction of MANETs. Secure routing, in particular, is an important and complicated issue. Organizing mobile nodes into manageable clusters can limit the amount of secure routing information. Under a cluster structure, mobile nodes are managed by nodes called cluster heads. The clusterhead role is resource consuming since its always switched on and is responsible for the long-range transmission. If a node has this role, it would burn its resource quickly, and after it died, all its members would be headless. In this work, we propose a new clustering algorithm for ad hoc networks, where the clusters are formed around the most powerful nodes, i.e, the node that has the best material resources such as residual energy, free memory, processor speed and hard disk space is elected as cluster head. Our algorithm takes into account the node range by including in our calculation the node density. Therefore, we are sure that the cluster head is represented by the most powerful and the less mobile node in the network that can perform its roles in the best conditions. This solution will be used to manage cryptographic keys in an ad hoc network. Our algorithm gives major improvements regarding the number and the performance of elected cluster heads. Analysis and simulation results are used to show the performance of our algorithm, compared with other clustering algorithms in literature.

Problem statement: Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. For effective data compression, the compression algorithm must be able to predict future data accurately in order to build a good probabilistic model for compression. Lossless compression is essential in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data could be deleterious. Approach: Prediction by Partial Matching (PPM) data compression technique had utmost performance standard and capable of very good compression on a variety of data. In this research, we had introduced PPM technique to compress the data and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The PPM algorithm was modeled using the hardware description language VHDL. Results: Functional simulations were commenced to verify the functionality of the system with both 16-bit input and 32-bit input. The FPGA utilized 1164 logic cells with a maximum system frequency of 95.3MHz on Altera FLEX10K. Conclusion: The proposed approach is computationally simple, accurate and exhibits a good balance of flexibility, speed, size and design cycle time.

Temporal stochastic linear encoding networks

Computer lab management work is trivial and complex with heavy workload. This paper will study the application of the hard disk recovery card technology in public computer labs of higher vocational schools associating with the ‘small sentinel’ hard disk recovery cards used in the computer lab of our department.

The conventional calculation of rotor losses in cage induction motors by the time-stepping finite element method requires the full slip waves of the rotor flux densities and currents. The calculations of the full slip waves are extremely expensive in CPU time, memory, and hard disk space for the induction motors with skewed slots and pulsewidth modulated supply. This article proposes a technique that reproduces those full slip waves by utilizing both the time varying and the spatial information of the rotor electromagnetic quantities and, hence, significantly saves the calculation cost. The more rotor bars per pair of poles the induction motor has, the more calculation cost this technique saves. The calculation costs of the proposed and conventional methods are compared for the rotor losses of a 5.5-kW inverter-fed skewed induction motor under load condition, and the calculated losses are validated by tests.

In this paper, we provide an efficient and easy-to-implement symmetric searchable encryption scheme (SSE) for string search, which takes one round of communication, $O(n)$ O ( n ) times of computations over $n$ n documents. Unlike previous schemes, we use hash-chaining instead of chain of encryption operations for index generation, which makes it suitable for lightweight applications. Unlike the previous SSE schemes for string search, with our scheme, server learns nothing about the frequency and the relative positions of the words being searched except what it can learn from the history . We are the first to propose probabilistic trapdoors in SSE for string search . We provide concrete proof of non-adaptive security of our scheme against honest-but-curious server based on the definitions of [12] . We also introduce a new notion of search pattern privacy , which gives a measure of security against the leakage from trapdoor. We have shown that our scheme is secure under search pattern indistinguishability definition. We show why SSE scheme for string search cannot attain adaptive indistinguishability criteria as mentioned in [12] . We also propose modifications of our scheme so that the scheme can be used against active adversaries at the cost of more rounds of communications and memory space. We validate our scheme against two different commercial datasets (see [1] , [2] ).

SPEC is the name of the operating system designed to control the NMR spectrometers in lab. SPEC is actually one large program which handles many functions necessary to control each spectrometer. The program handles all I/O with peripheral devices such as the console ('terminal' or 'CRT'). The program carries out its operations by accepting commands which each invoke specific subroutines to perform their function. There are a total of 60 commands in SPEC, each carrying out a different function. Because so many commands make SPEC a very large program, not all of the program is core resident. Rather, each command calls in an overlay handler which loads into memory the appropriate overlay from the disk and begins execution of the command. Thus SPEC is an independent disk based operating system. The commands in SPEC are capable of operating the microprocessor based pulse programmer, starting and acquiring data from the spectrometer data acquisition system, storing data on disk and manipulating it mathematically, displaying and plotting data. All arithmetic operations within SPEC are performed on integers. Since the DATA GENERAL computers are 16 bit machines operating in two's complement mode, the integer range is +32767. Many of the mathematical operations of SPEC are done in double precision integer mode with the final result always scaled to the above range. For many of the commands, integer overflow is detected and reported as an error message. Overflowed points are set to +32767. SPEC accepts command input from the console or reads a string of commands previously entered on the disk. The later command structure is called a macro. Macros may be nested and may have constants passed to them at execution time, thus allowing for a powerful supercommand structure. Both forms of commands are discussed in the next section. SPEC is designed to run on a DATA GENERAL computer with 32K words of memory and a 10Mbyte hard disk system. There are minor differences between the various computers in lab which are discussed in one of the appendices. The disk systems consist of two. 5Mbyte platters, one fixed and one in a removable pack. While running SPEC, all disk I/O is with the fixed disk. All pulse programs, data records, phase box records, etc. reside on the fixed disk. Thus each disk drive is specific to one spectrometer. When running under RDOS (the DG operating system) these disk files may also be accessed. All the source listings of SPEC subroutines, the SPEC program library, and all supporting programs reside on one of the removable packs (labeled 'SPEC'). This pack will normally be in the drive but it is not required to run SPEC. It is required to run any of the supporting programs discussed in another chapter. This manual attempts to document the SPEC operating system and related topics. The following section discusses the general operation of SPEC including how to get into the program and how to issue commands. The following chapters discuss the commands in detail, operation of the microprocessor based pulse programmer, spectrometer peripherals, supporting programs, and how to create and load a 'TEMP' program. The appendices contain details on bootstrapping procedures, hardware specifics, a technical description of SPEC and how to change or recreate it, a description of microcode loading and the procedure for formatting floppy, disks and recreating RDGS.

Nowadays, the increase of quality and resolution of remote sensing image will lead to the data's capacity extremely increase; A single computer, even a very expensive minicomputer or a PC server, is difficult to meet the disk space, memory space and processing time requirements when faced with this situation. Cloud computing can give us a new resolution to these problems. In this study, we proposed a Hadoop-based remote sensing image processing system (HBRSIPS), on the one hand, it re-defines a storage structure to adapt the characters of remote sensing images, on the other hand, We construct Java program classes based on MapReduce architecture, remote sensing image processing algorithms can be easily applied to the cloud computing environment. The experiments show, the proposed system can save remote sensing image into Hadoop's distribute storage environment and algorithms based on cloud environment were faster than single process algorithm.

Chapter 10 - Memory Manager

Discussion of “On-line computers in the animal laboratory”

As machine learning (ML) models are used more and more for important business tasks, it is important to make sure that ML operations are safe. This makes it easier to automate tasks, make decisions, and come up with fresh ideas in a number of fields. These procedures generally comprise sensitive datasets, proprietary algorithms, and complex pipelines that go through many phases, such as taking in data, preparing it, training it, validating it, and deploying it. These pipelines are open to data corruption, model changes, and illegal access since they don't have strong security measures in place. All of these things might make the models substantially less accurate and reliable. Kubernetes is the best platform for building and scaling machine learning operations because it has strong container orchestration features, declarative configuration, and works with the latest MLOps tools. It is always changing and falling apart, however, which means it has its own security problems that need to be dealt with carefully and with careful preparation. This article talks about how to use Kubernetes and follow the best practices and principles of the Certified Kubernetes Security Specialist (CKS) framework. This post's purpose is to provide you knowledge on how to make machine learning pipelines safer. We will talk about how to set up strong Role-Based Access Control (RBAC) based on the principle of least privilege, how to create detailed network policies to keep workloads separate, how to safely manage secrets using both native Kubernetes tools and third-party tools, and how to use runtime security measures like container image scanning and admission controllers to make sure that everything stays safe at all times. In the context of building machine learning systems that can solve problems, we want to stress how important DevSecOps concepts are. This list includes things like putting security first, automating the process of finding vulnerabilities, and always keeping an eye on pipelines. In this case, the security rules that come with Kubernetes are applied. Companies may build machine learning systems that are scalable, auditable, and safe utilizing methods that CKS has approved. This lowers risks and encourages new ideas. There is a case study at the end of the post that shows how a tiered security policy may make a real-world machine learning pipeline better. This is solid advice for anybody who is working with Kubernetes-based machine learning systems when it comes to putting security first

Most computer systems currently consist of DRAM as main memory and hard disk drives (HDDs) as storage devices. Due to the volatile nature of DRAM, the main memory may suffer from data loss in the event of power failures or system crashes. With rapid development of new types of non-volatile memory (NVRAM), such as PCM, Memristor, and STT-RAM, it becomes likely that one of these technologies will replace DRAM as main memory in the not-too-distant future. In an NVRAM based buffer cache, any updated pages can be kept longer without the urgency to be flushed to HDDs. This opens opportunities for designing new buffer cache policies that can achieve better storage performance. However, it is challenging to design a policy that can also increase the cache hit ratio. In this paper, we propose a buffer cache policy, named I/O-Cache, that regroups and synchronizes long sets of consecutive dirty pages to take advantage of HDDs' fast sequential access speed and the non-volatile property of NVRAM. In addition, our new policy can dynamically separate the whole cache into a dirty cache and a clean cache, according to the characteristics of the workload, to decrease storage writes. We evaluate our scheme with various traces. The experimental results show that I/O-Cache shortens I/O completion time, decreases the number of I/O requests, and improves the cache hit ratio compared with existing cache policies.

This paper describes the architecture of a Web-based audio/video educational system for real-time laboratory experiments through the Internet. The goal of the WAVES (Web-based Audio Video Educational Systems) project is to develop an open and integrated platform for online laboratory experimentation, computer simulation, and course instruction using the Internet for research and teaching in modeling, design, analysis, and control of dynamical systems. The project is unique in that students can conduct hardware lab experiments via the Internet and receive real-time data feedback and audio/video feedback of experimented results. Although many similar Internet-based tools have been developed throughout the last two years, very few of them encompass hands-on experiments with actual laboratory hardware. Since Internet-based hardware systems can be shared and scheduled for use by a large number of researchers and students in various locations, the WAVES platform is a cost-effective way to address the issue of hands-on experience for students. In our current implementation, a three layer architecture is used, i.e., the clients, the dispatcher, and the coordinators. First, the clients will issue tasks and receive feedback for conducting a specific lab experiment; then the dispatcher deals with the control and communication between the clients and coordinators; finally the coordinators conduct the actual experiment through hardware setups, simulation models, and database systems.

Aims: The rise of automation has greatly impacted various aspects of daily life, including attendance management systems. Traditional methods like manual roll calls and card swiping are often inefficient and prone to errors, leading researchers to explore innovative alternatives such as face recognition technology. This technology employs advanced algorithms and image processing techniques to accurately identify individuals based on their unique facial features, thereby enhancing accuracy and security measures. The objective of this project is to develop a face recognition-based attendance system with specific goals, including circuit design, simulation using Proteus Software, implementation, and performance evaluation through rigorous field-testing. Study Design: The system comprises an input subsystem with a Camera module and two output subsystems: an LCD and a Web-database PC software. Initially, custom and stored image data of students are saved in a microSD card of the camera module, and attendance records are updated on a web-database upon successful scanning and recognition of students' faces. Methodology: The implementation process involves assembling various hardware components, including securely mounting the ESP32 CAM and integrating a 16x4 Character LCD Display for visual feedback. LEDs are used as visual indicators for system statuses, while a Power Bank Module ensures consistent power supply. The system is controlled by the ESP32-CAM, which captures and verifies faces, displays actions on the LCD, and updates the web-database with attendance data. Results: Testing results indicate the system's high accuracy, achieving a 100% attendance rate across multiple class sessions with zero misclassifications. Conclusion: The user-friendly interface and seamless wireless connectivity enhance accessibility and real-time monitoring, making it a valuable tool for classroom management and office use. The research has some hardware limitations which includes processing large amounts of facial data requires robust hardware. Inadequate camera resolution or processing power can lead to slow performance or inaccurate recognition. Overall, the face recognition-based attendance system has the potential to revolutionize traditional methods, offering high accuracy, user-friendly features, and robust connectivity, with possible applications beyond education through further refinement and research. Facial recognition-based attendance systems are more accurate and efficient than traditional methods like manual roll calls or card swiping. By automating attendance, they reduce errors and provide real-time updates with web integration. Despite hardware limitations, the system's accuracy and ease of use make it a superior alternative with potential for broader applications.