Mathematical reasoning has been challenging for large language models (LLMs), and the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs.However, current approaches either necessitate extensive inference datasets for training or depend on few-shot methods that frequently compromise computational accuracy.To address these fundamental limitations, we propose Step Guided Reasoning, a novel training-free adaptation framework that efficiently equips generalpurpose pre-trained language models with enhanced mathematical reasoning capabilities.In this approach, LLMs reflect on small reasoning steps, similar to how humans deliberate and focus attention on what to do next.By incorporating this reflective process into the inference stage, LLMs can effectively guide their reasoning from one step to the next.Through extensive experiments, we demonstrate the significant effect of Step Guided Reasoning in enhancing mathematical performance in state-of-the-art language models -Qwen2-72B-Instruct outperforms its math-specific counterpart, Qwen2.5-72B-Math-Instruct, on MMLU-STEM with a score of 90.9%, compared to 87.3%.The average scores of Qwen2-7B-Instruct and Qwen2-72B-Instruct increase from 27.1% to 36.3% and from 36.5% to 47.4% in the math domain, respectively.

The proliferation of Internet of Things (IoT) devices with weak passwords poses significant challenges to network security. Hackers can easily compromise these IoT devices by brute-forcing their passwords and then utilize them to launch severe attacks such as Distributed Denial of Service (DDoS). To defeat such threats, an effective solution is to ensure that each device has a strong, high-strength password. However, in practice, this poses a significant challenge, as configuring and managing passwords for such a vast number of devices is highly complex. In this work, we propose a novel Multi-Device Collaborative Authentication (MDCA) system that enables IoT devices to be protected by strong passwords without altering their original weak ones. Our approach is inspired by group defense strategies prevalent in nature: animals cooperate and form groups to enhance their chances of survival. Intuitively, IoT devices can also collaborate to improve their security. Specifically, in our design, devices with strong passwords provide authentication for devices having weak credentials, and the latter can generate clues to detect brute-force attacks. Once these attacks are detected, requests to IoT devices are first authenticated by several other strong password protected devices, and if all authentications succeed, the requests are then permitted. We have implemented the proposed system and theoretically analyzed its security. The experimental results match the theoretical analysis well.

Recent research has shown that hackers can ef-ficiently infer sensitive user activities only by observing the network traffic of smart home devices. To protect users' privacy, researchers have designed several traffic obfuscation methods. However, existing methods usually consume high bandwidth or provide weak privacy protection. In this paper, we conduct thorough research on smart home traffic obfuscation. We first propose a fixed-value obfuscation scheme and prove that it is perfectly secure by showing the indistinguishability of user activities. Yet, fixed-value obfuscation has high bandwidth con-sumption. To further reduce the bandwidth consumption, we propose combining fixed-value obfuscation with Multipath TCP transmission. The security and performance of the proposed mul-tipath fixed-value obfuscation method are theoretically analyzed. We have implemented the proposed methods and tested them on public packet traces and simulated smart home networks. The experimental results match well with the theoretical analysis.