Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer. However, the high computation and resource consumption severely limit its practical application, especifically in regions with limited medical and computational resources. In this study, with the help of cloud computing and IoT technologies, we develop a novel blood cancer dynamic monitoring diagnostic model named BloodPatrol based on an intelligent feature weight fusion mechanism. The proposed model is capable of capturing the dual-view importance relationship between cell samples and features, greatly improving prediction accuracy and significantly surpassing previous models. Besides, benefiting from the powerful processing ability of cloud computing, BloodPatrol can run on a distributed network to efficiently process large-scale cell data, which provides immediate and scalable blood cancer diagnostic services.

System-level emulators have been used extensively for the design, debugging and evaluation of the system software. They work by providing a system-level virtual machine that can support a guest operating system (OS) running on a platform with the same or different native OS using the same or different instruction-set architecture. For such a system-level emulation, dynamic binary translation (DBT) is one of the core technologies. A recently proposed learning-based approach using automatically-learned translation rules has shown to improve DBT performance significantly with much higher quality translated code. However, it has only been used on user-level emulation, not system-level emulation. In applying this approach directly on QEMU for system-level emulation, we find it actually causes an unexpected performance degradation of 5% on average. By analyzing its main culprits in more detail, we find that the learning-based approach will by default use host registers to maintain the guest CPU states that include condition-code registers (or FLAG registers). In cases where QEMU needs to be involved (in which QEMU also needs to use the host registers), maintaining system states in the host registers for the guest, the host and QEMU during and between the context switches can cause undue overheads, if not handled carefully. Such cases include emulating system-level instructions, address translation and interrupts, which require the use of QEMU's helper functions. To achieve the intended performance improvement through better-quality code generated by the learning-based approach, we propose several optimization techniques that include reducing the overhead incurred in each context switch, the number of needed context switches, and better code scheduling to eliminate context switches. Our experimental results show that such optimizations can achieve an average of 1.36X speedup over QEMU 6.1 using SPEC CINT2006 and 1.15X on real-world applications in the system emulation mode.

With the rapid development of LLMs, the evaluation of factual consistency between source documents and generated texts plays a more crucial role in natural language generation (NLG). Recent methods usually suffer from low quality and insufficient quantity of training data. In this paper, we propose a method for synthesizing factual consistency data by harnessing the vast knowledge stored within large language models. The synthetic data produced through this approach demonstrates notable discriminative abilities and robustness for training the factual consistency evaluation model. We adopt experiments on two benchmark datasets (TRUE and SummaC) and our method achieves 3.5% and 4.6% relative improvement on AUC-ROC metric respectively.

Recently, the development of foundation models has led to significant advances in the ability of artificial intelligence (AI) to generate multimodal content such as text and images. However, specialized industrial scenarios such as finance, which require high levels of security and compliance, pose challenges for the application of generative AI due to its uncontrollability. To address this issue, we propose FinGuard, a multimodal AI-generated content (AIGC) guardrail specifically designed for financial scenarios. We provide detailed definitions of the general quality, financial compliance, and security dimensions of AIGC, and implement the evaluation and inspection of multimodal AIGC including text and images. Our proposed FinGuard has been applied to a financial marketing application serving hundreds of millions of users.

Dynamic binary translation (DBT) is widely used in system virtualization and many other important applications. To achieve a higher translation quality, a learning-based approach has been recently proposed to automatically learn semantically-equivalent translation rules. Because translation rules directly impact the quality and performance of the translated host codes, one of the key issues is to collect as many translation rules as possible through minimal training data set. The collected translation rules should also cover (i.e. apply to) as many guest binary instructions or code sequences as possible at the runtime. For those guest binary instructions that are not covered by the learned rules, emulation has to be used, which will incur additional runtime overhead. Prior learning-based DBT systems only achieve an average of about 69% dynamic code coverage for SPEC CINT 2006.In this paper, we propose a novel parameterization approach to take advantage of the regularity and the well-structured format in most modern ISAs. It allows us to extend the learned translation rules to include instructions or instruction sequences of similar structures or characteristics that are not covered in the training set. More translation rules can thus be harvested from the same training set. Experimental results on QEMU 4.1 show that using such a parameterization approach we can expand the learned 2,724 rules to 86,423 applicable rules for SPEC CINT 2006. Its code coverage can also be expanded from about 69.7% to about 95.5% with a 24% performance improvement compared to enhanced learning-based approach.