1,025 skills found · Page 11 of 35
crabby-lang / CrabbyCrabby Programming Language.
hzhou / MyDefProgramming in the next paradigm -- your way
bloominstituteoftechnology / AlgorithmsAn introduction to algorithmic problem-solving and algorithmic paradigms.
KevenJAntenor / Functional Dynamics In OCamlThe project includes a variety of recursive functions to compute and manipulate sequences, count specific occurrences within lists, and apply transformations, demonstrating the elegance and power of functional paradigms.
gassechen / IiscvThe main objective of the IISCV (Lisp-based Version Control System) project is to "revive the image-based software development paradigm, inherent to Lisp, and adapt it with a layer of internal and external auditability.
LudovicTuncay / Audio JEPAAudio-JEPA is an adaptation of the Joint-Embedding Predictive Architecture (JEPA) for self-supervised audio representation learning. Built upon the I-JEPA paradigm, it uses a Vision Transformer (ViT) backbone to predict latent representations of masked spectrogram patches.
jtauber / Greek InflexionPython library for generating (and analyzing) Ancient Greek inflectional paradigms
alonsd / BasicApplicationBasic Android + iOS temple application using latest architecture and paradigms
OntoUML / Ontouml Vp PluginA plugin for Visual Paradigm to add support for OntoUML modeling and model intelligence services
TCP1P / TCP1P CTF Blockchain InfraThis repository contains the setup from Paradigm CTF blockchain challenges based on the original repository. We've introduced new features, including a web interface and additional challenge setup.
oci-landing-zones / Terraform Oci Core LandingzoneThe OCI Core Landing Zone unifies the OCI landing zone initiatives that follow a centralized deployment paradigm for provisioning the base tenancy, including CIS Landing Zone and Oracle Enterprise Landing Zone. For deploying landing zones in multiple stacks, see The OCI Operating Entities Landing Zone.
Gaurav14cs17 / YOLOEPP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful backbone and neck equipped with CSPRepResStage, ET-head and dynamic label assignment algorithm TAL.
Zard-C / CS107CS107 - Programming Paradigms https://see.stanford.edu/Course/CS107
TKKim93 / DivMatchDiversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
alexcaselli / Federated Learning For Human Mobility ModelsThanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
WASdev / Sample.daytrader7The DayTrader 7 benchmark sample, which is a Java EE 7 application built around the paradigm of an online stock trading system. #JavaEE7
Atlas-LiftTech / MQTTnet.AspNetCore.AttributeRoutingEasily create Controllers and Actions to process incoming MQTT messages using a familiar paradigm and MQTTnet
liguge / WIDANInterpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Fault Diagnosis （故障诊断）
yixuan730 / DetToolChainDettoolchain: A new prompting paradigm to unleash detection ability of MLLM
soliditee / Paradigm Ctf 2022 0xmonacoContract for Smart Cars in the 0xmonaco challenge from Paradigm CTF 2022
