Search results for " learning."
showing 10 items of 5179 documents
How can algorithms help in segmenting users and customers? : A systematic review and research agenda for algorithmic customer segmentation
2023
What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent…
Towards digital cognitive clones for the decision-makers: adversarial training experiments
2021
Abstract There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveilla…
Distance Learning Improvement Possibilities in Sciences for 8th to 12th Grades
2021
COVID-19 pandēmijas ietekmē, cilvēku ikdiena vairs nav iedomājama bez attālinātā darba un attālinātajām mācībām. Svarīgi, lai attālinātās mācības neradītu negatīvu ietekmi uz izglītības kvalitāti, īpaši specifiskos mācību priekšmetos, kad to veiksmīgai apgūšanai ir nepieciešami arī praktiskie darbi un laboratorijas darbi. Līdz ar to bija svarīgi izpētīt attālināto mācību specifiku un pilnveidošanas iespējas dabaszinību mācību priekšmetos, lai varētu izstrādāt rekomendācijas dažādām sociālajām grupām – pedagogiem, skolniekiem un viņu vecākiem, lai nodrošinātu pilnvērtīgu dabaszinātņu mācību priekšmetu apguvi. Autore pētījumā izmantoja gadījuma pētījuma dizainu, kurā atklāja, ka Latvijā šobrī…
Algorytmy — nowy wymiar nadzoru i kontroli nad świadczącym pracę
2020
Autor wskazuje, że algorytmy stają się kluczową technologią władzy nad świadczącym pracę. Pozwalają na sformatowanie zarówno samych pracowników, jak i wzajemnych oddziaływań między nimi zasadniczo w jednym celu — optymalizacji procesów pracy służących zwiększeniu wydajności. Z tej perspektywy pracownik jest cyfrowym modelem zbudowanym z danych i informacji. Oznacza to, że wszelkie jego ekspresje ujawniane w środowisku pracy będą mogły być mierzalne, i to na rożne sposoby. Algorytmy rzucają również nowe światło na zagadnienie podporządkowania w zatrudnieniu. A wszystko dzięki ,,wtapianiu się” ich w środowisko danych biometrycznych osób świadczących pracę. W pewien sposób przejmują one własno…
Communication-Efficient Federated Learning in Channel Constrained Internet of Things
2022
Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize comp…
Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance.
2021
The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support…
On Attacking Future 5G Networks with Adversarial Examples : Survey
2022
The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in …
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
2020
Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data platform service. Adversarial examples are malicious inputs to ML-models that provide erroneous model outputs while appearing to be unmodified. This kind of attack can fool the classifier and can prevent ML-models from generalizing well and from learning high-level representation; instead, the ML-model learns superficial dataset regularity. This study focuses on investigating, detecting, and preventing adversarial attacks towards a cloud dat…
An empirical study of the understanding of formal propositions about sequences, with a focus on infinite limits
2018
International audience; In this paper, we analyze the answers of one group of high-school students and two groups of first-year University students to a questionnaire designed to test their level of recognition and understanding of the formal definition of the concept of infinite limit. Although this empirical study is ancillary to a larger project centred on didactic engineering, its analysis sheds light on the key issue of the logical prerequisites for the learning of the fundamental concepts of analysis. It also provides a new tool to investigate students' concept-image of limits, and assess the impact of teaching contexts and teaching paths.
Are all denumerable sets of numbers order-isomorphic?
2018
International audience; In this paper we study cognitive conflicts on the issue of number sets being dense, ordered and denumerable. We first provide historical-epistemological background related to these notions. Then we consider the cognitive conflicts under the lenses of concept image and concept definition, which we use to analyse empirical data collected in order to understand better the didactical and cognitive issues at stake.