Understanding how Transformer-based language models store and retrieve factual associations is critical for improving interpretability and enabling targeted model editing.Prior work, primarily on GPT-style models, has identified MLP modules in early layers as key contributors to factual recall.However, it remains unclear whether these findings generalize across different autoregressive architectures.To address this, we conduct a comprehensive evaluation of factual recall across several models-including GPT, LLaMA, Qwen, and DeepSeek-analyzing where and how factual information is encoded and accessed.Consequently, we find that Qwen-based models behave differently from previous patterns: attention modules in the earliest layers contribute more to factual recall than MLP modules.Our findings suggest that even within the autoregressive Transformer family, architectural variations can lead to fundamentally different mechanisms of factual recall. 1

Federated learning is a promising approach for training machine learning models while preserving data privacy. However, its distributed nature makes it vulnerable to backdoor attacks, particularly in NLP tasks, where related research remains limited. This paper introduces SDBA, a novel backdoor attack mechanism designed for NLP tasks in federated learning environments. Through a systematic analysis across LSTM and GPT-2 models, we identify the most vulnerable layers for backdoor injection and achieve both stealth and long-lasting durability by applying layer-wise gradient masking and top-k% gradient masking. Also, to evaluate the task generalizability of SDBA, we additionally conduct experiments on the T5 model. Experiments on next-token prediction, sentiment analysis, and question answering tasks show that SDBA outperforms existing backdoors in terms of durability and effectively bypasses representative defense mechanisms, demonstrating notable performance in transformer-based models such as GPT-2. These results highlight the urgent need for robust defense strategies in NLP-based federated learning systems.

Since its emergence, NoSQL databases have firmly established themselves as an indispensable software component of modern cloud-native applications. However, it also becomes increasingly challenging to perform critical management tasks such as troubleshooting unexpected performance problems. This is due to the ever-increasing diversity and specialization of NoSQL databases that make it difficult to observe the internal activities. To address these challenges, we have designed and built a technique for introspecting NoSQL databases. Our technique traces system call sequences of key operations under controlled workloads and filters scaling patterns from constant components. Novel algorithms are developed to uncover repeating patterns of system calls from massive amounts of traces and filter out background noise with high efficiency. The evaluation shows that our technique can greatly enhance the visibility into the NoSQL databases enabling us to diagnose performance problems or gain insights into internal activities.

Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.

Over the recent years, cloud storage services have become increasingly popular, where users can outsource data and access the outsourced data anywhere, anytime. Accordingly, the data in the cloud is growing explosively. Among the outsourced data, most of them are duplicated. Cloud storage service providers can save huge amounts of resources via client-side deduplication. On the other hand, for safe outsourcing, clients who use the cloud storage service desire data integrity and confidentiality of the outsourced data. However, ensuring confidentiality and integrity in the cloud storage environment can be difficult. Recently, in order to achieve integrity with deduplication, the notion of deduplicatable proof of storage has emerged, and various schemes have been proposed. However, previous schemes are still inefficient and insecure. In this paper, we propose a symmetric key based deduplicatable proof of storage scheme, which ensures confidentiality with dictionary attack resilience and supports integrity auditing based on symmetric key cryptography. In our proposal, we introduce a bit-level challenge in a deduplicatable proof of storage protocol to minimize data access. In addition, we prove the security of our proposal in the random oracle model with information theory. Implementation results show that our scheme has the best performance.

Multiplication schemes based on Toeplitz matrix-vector product (TMVP) have been proposed by many researchers. TMVP can be computed using the recursive two-way and three-way split methods, which are composed of four blocks. Among them, we improve the space complexity of the component matrix formation (CMF) block. This result derives the improvements of multiplication schemes based on TMVP. Also, we present a subquadratic space complexity$GF(2^m)$multiplier with even type Gaussian normal basis (GNB). In order to design the multiplier, we formulate field multiplication as a sum of two TMVPs and efficiently compute the sum. As a result, for type 2 and 4 GNBs, the proposed multipliers outperform other similar schemes. The proposed type 6 GNB is the first subquadrtic space complexity multiplier with its explicit complexity formula.

In this paper, we present new parallel polynomial multiplication formulas which result in subquadratic space complexity. The schemes are based on a recently proposed block recombination of polynomial multiplication formula. The proposed two-way, three-way, and four-way split polynomial multiplication formulas achieve the smallest space complexities. Moreover, by providing area-time tradeoff method, the proposed formulas enable one to choose a parallel formula for polynomial multiplication which is suited for a design environment.

We propose block decompositions for the Toeplitz matrix-vector product (TMVP) using the$k$-way splitting method presented in the above paper. As a result, we show that the space complexity for TMVP can be improved.

In the above paper, Akleylekproposed an efficient multiplication algorithm for polynomials in Chebyshev form. In this comment, we show that a recombination of the above proposed algorithm induces more efficient algorithm for the multiplications of polynomials in Chebyshev form.

We have derived a lower bound on the linear span of a binary sequence generated by a feedback with carry shift register (FCSR) under the following condition: q is a power of a prime such that q=r/sup e/(e/spl ges/2) and r(=2p+1), where both r and p are 2-prime. This allows us to design FCSR stream ciphers similar to previously proposed linear feedback shift register (LFSR) stream ciphers.