175 research outputs found

    Marketing Plan for Columbus State University

    Get PDF
    Marketing is an important tool for capturing customers and sales in business, and it’s equally as important to an institution like Columbus State University (CSU) aiming to capture student customers. The development of a marketing plan through extensive research and planning will contribute to the success of all involved marketing efforts. This marketing plan will include all the standard essential elements: business mission statement, situation (SWOT) analysis, objectives, marketing strategy, implementation, and evaluation control. For the mission statement, the current statement from the 2018-2023 Strategic Plan will be used. The situation analysis will include looking at the current strengths, weakness, opportunities, and threats of CSU. Based on this analysis, the objectives of the marketing plan will be created. The marketing strategy will include development of the target market strategy and the marketing mix. Target market strategy will consist of segmentation, targeting, and positioning while the marketing mix is made up of product, price, place, and promotion. An action plan will be developed to implement the marketing plan, and evaluation and control methods will be planned. The proposed plan would increase student enrollment and brand awareness of CSU

    NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels

    Full text link
    Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for designing effective learning objectives. However, empirical results of AT always exhibit the robustness at odds with accuracy and the existence of the cross-over mixture problem, which motivates us to study some label randomness for benefiting the AT. First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain the observations on when NL injection benefits AT. Second, based on the observations, we propose a simple but effective method -- NoiLIn that randomly injects NLs into training data at each training epoch and dynamically increases the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can significantly mitigate the AT's undesirable issue of robust overfitting and even further improve the generalization of the state-of-the-art AT methods. Philosophically, NoiLIn sheds light on a new perspective of learning with NLs: NLs should not always be deemed detrimental, and even in the absence of NLs in the training set, we may consider injecting them deliberately. Codes are available in https://github.com/zjfheart/NoiLIn.Comment: Accepted at Transactions on Machine Learning Research (TMLR) at June 202

    A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems

    Get PDF
    Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelĂąs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels. An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm

    An open virtual testbed for industrial control system security research

    Get PDF
    ICS security has been a topic of scrutiny and research for several years, and many security issues are well known. However, research efforts are impeded by a lack of an open virtual industrial control system testbed for security research. This thesis describes a virtual testbed framework using Python to create discrete testbed components (including virtual devices and process simulators). This testbed is designed such that the testbeds are interoperable with real ICS devices and that the virtual testbeds can provide comparable ICS network behavior to a laboratory testbed. Two testbeds based on laboratory testbeds have been developed and have been shown to be interoperable with real industrial control systemequipment and vulnerable to attacks in the samemanner as a real system. Additionally, these testbeds have been quantitatively shown to produce traffic close to laboratory systems (within 90% similarity on most metrics)

    State of Academic Affairs Report

    Get PDF
    A comprehensive overview of the Office for Academic Affairs covering the years 2013 through 2015 as it is described by the Faculty Hanbook policy A83 Annual Reports. This includes reports on all 12 schools and colleges, and on all administrative units including Enrollment Management and GEO

    2021 Fifth-Year Interim Report, Narratives only (238 pages)

    Get PDF

    Bench-Ranking: ettekirjutav analĂŒĂŒsimeetod suurte teadmiste graafide pĂ€ringutele

    Get PDF
    Relatsiooniliste suurandmete (BD) töötlemisraamistike kasutamine suurte teadmiste graafide töötlemiseks kĂ€tkeb endas vĂ”imalust pĂ€ringu jĂ”udlust optimeerimida. Kaasaegsed BD-sĂŒsteemid on samas keerulised andmesĂŒsteemid, mille konfiguratsioonid omavad olulist mĂ”ju jĂ”udlusele. Erinevate raamistike ja konfiguratsioonide vĂ”rdlusuuringud pakuvad kogukonnale parimaid tavasid parema jĂ”udluse saavutamiseks. Enamik neist vĂ”rdlusuuringutest saab liigitada siiski vaid kirjeldavaks ja diagnostiliseks analĂŒĂŒtikaks. Lisaks puudub ĂŒhtne standard nende uuringute vĂ”rdlemiseks kvantitatiivselt jĂ€rjestatud kujul. Veelgi enam, suurte graafide töötlemiseks vajalike konveierite kavandamine eeldab tĂ€iendavaid disainiotsuseid mis tulenevad mitteloomulikust (relatsioonilisest) graafi töötlemise paradigmast. Taolisi disainiotsuseid ei saa automaatselt langetada, nt relatsiooniskeemi, partitsioonitehnika ja salvestusvormingute valikut. KĂ€esolevas töös kĂ€sitleme kuidas me antud uurimuslĂŒnga tĂ€idame. Esmalt nĂ€itame disainiotsuste kompromisside mĂ”ju BD-sĂŒsteemide jĂ”udluse korratavusele suurte teadmiste graafide pĂ€ringute tegemisel. Lisaks nĂ€itame BD-raamistike jĂ”udluse kirjeldavate ja diagnostiliste analĂŒĂŒside piiranguid suurte graafide pĂ€ringute tegemisel. SeejĂ€rel uurime, kuidas lubada ettekirjutavat analĂŒĂŒtikat jĂ€rjestamisfunktsioonide ja mitmemÔÔtmeliste optimeerimistehnikate (nn "Bench-Ranking") kaudu. See lĂ€henemine peidab kirjeldava tulemusanalĂŒĂŒsi keerukuse, suunates praktiku otse teostatavate teadlike otsusteni.Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisions’ trade-offs on the BD systems’ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworks’ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called ”Bench-Ranking”). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.https://www.ester.ee/record=b553332

    UMSL Bulletin 2020-2021

    Get PDF
    The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp
    • 

    corecore