Logic obfuscation is a crucial technique in hardware security aimed at shielding integrated circuits (ICs) from threats such as reverse engineering and intellectual property theft. This research introduces an innovative approach by incorporating transmission gates into logic obfuscation mechanisms to bolster security within SRAM arrays. We propose and implement a distinctive locking mechanism utilizing transmission gates, applied to the word lines of a 4x4 SRAM array. This mechanism effectively regulates access to the memory cells, ensuring that data can only be read or written with the correct key input. To assess the efficacy of our approach, we evaluate the transistor count, power consumption, write access time, and read access time of the obfuscated SRAM array in comparison to a conventional, non-obfuscated SRAM array. Our experimental results reveal that, although the locking mechanism leads to a slight increase in power consumption, write access time, and read access time, these changes are minimal and within the range of typical circuit variations. The proposed method significantly enhances SRAM array security with only a marginal impact on performance, demonstrating its potential for safeguarding sensitive data in digital systems

Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency ( tf ) from the relevant documents is much smaller than the upper bound of the normalized tf from the whole collection. As a result, the existing models suffer from two major problems: (i) the domain mismatch causes data modeling error, (ii) since the outliers have very large magnitude and the retrieval models follow tf hypothesis, the combination of these two factors tends to overestimate the relevance score. In an attempt to address these problems, we propose novel weighted probabilistic models based on truncated distributions. We evaluate our models on a set of large document collections. Significant performance improvement over six existing probabilistic models is demonstrated.

Financial reports filed by various companies discuss compliance, risks, and future plans, such as goals and new projects, which directly impact their stock price. Quick consumption of such information is critical for financial analysts and investors to make stock buy/sell decisions and for equity evaluations. Hence, we study the problem of extractive summarization of 10-K reports. Recently, Transformer-based summarization models have become very popular. However, lack of in-domain labeled summarization data is a major roadblock to train such finance-specific summarization models. We also show that zero-shot inference on such pretrained models is not as effective either. In this paper, we address this challenge by modeling 10-K report summarization using a goal-directed setting where we leverage summaries with labeled goal-related data for the stock buy/sell classification goal. Further, we provide improvements by considering a multi-task learning method with an industry classification auxiliary task. Intrinsic evaluation as well as extrinsic evaluation for the stock buy/sell classification and portfolio construction tasks shows that our proposed method significantly outperforms strong baselines.