Namely app5/18/2023 ![]() ![]() □ Charlie focuses on the needs of small businesses Why UK-Based small businesses should consider software designed for startupsĬharlieHR is ideal for small businesses wanting to run their HR admin without complexity. Other HR alternatives are focused specifically on the unique challenges faced by small businesses. The product is not designed for smaller companies with unique HR needs like having a one-person (or non-existent!) HR team and needing a solution to work seamlessly out the box. Namely’s HR solution is aimed at mid-sized companies with 50 - 1000 employees. ![]() It's aimed at mid-sized companies, not startups It’s not ideal for companies whereby building a company culture alongside core HR is a focus. Unlike some alternatives, the platform doesn’t offer any surveys or polls to gather employee feedback. Namely lacks features that focus on improving employee engagement and building a company culture. It lacks features for employee engagement If you’re looking for an HR platform that listens to feedback and makes regular improvements, there may be better alternatives for you. User reviews note Namely’s Benefits is difficult to use without much training, and the performance review function (where managers can send customised questions to employees for feedback) is challenging to set up.Ĭustomers have also commented on the lack of regular product improvements. Namely’s platform has lots of features but these don’t always work as smoothly as expected. The product is clunky and not updated regularly Alternatives offer onboarding processes designed to ensure new joiners have all the information they need directly from their HR platform. This means some information needs to be manually communicated to new joiners (not ideal if you’re a one-person HR team at a small company!). For example, there is no handbook integration to give employees visibility on company policies straight away, and no checklist to keep track of onboarding tasks. When new employees first sign up to Namely, the onboarding steps aren’t as clear and automated as other HR software out there. The onboarding process isn’t easy for new joiners Many features will be redundant if your company in under 50 employees.It lacks quality features for employee engagement.The product is clunky to use and isn’t updated regularly.The onboarding process isn’t easy for new joiners.The platform also falls short when it comes to high-quality support. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.Namely offers plenty of core HR features but these aren’t always intuitive to use compared to alternatives. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. ![]() We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. The security risks stemming from them have not been explored. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. Network and Distributed System Security (NDSS) Symposiumĭataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. Yugeng Liu (CISPA Helmholtz Center for Information Security), Zheng Li (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yun Shen (Netapp), Yang Zhang (CISPA Helmholtz Center for Information Security)
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