134 research outputs found
A review of research on acoustic detection of heat exchanger tube
Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube
AI Chain on Large Language Model for Unsupervised Control Flow Graph Generation for Statically-Typed Partial Code
Control Flow Graphs (CFGs) are essential for visualizing, understanding and
analyzing program behavior. For statically-typed programming language like
Java, developers obtain CFGs by using bytecode-based methods for compilable
code and Abstract Syntax Tree (AST)-based methods for partially uncompilable
code. However, explicit syntax errors during AST construction and implicit
semantic errors caused by bad coding practices can lead to behavioral loss and
deviation of CFGs.To address the issue, we propose a novel approach that
leverages the error-tolerant and understanding ability of pre-trained Large
Language Models (LLMs) to generate CFGs. Our approach involves a Chain of
Thought (CoT) with four steps: structure hierarchy extraction, nested code
block extraction, CFG generation of nested code blocks, and fusion of all
nested code blocks' CFGs. To address the limitations of the original CoT's
single-prompt approach (i.e., completing all steps in a single generative
pass), which can result in an ``epic'' prompt with hard-to-control behavior and
error accumulation, we break down the CoT into an AI chain with explicit
sub-steps. Each sub-step corresponds to a separate AI-unit, with an effective
prompt assigned to each unit for interacting with LLMs to accomplish a specific
purpose.Our experiments confirmed that our method outperforms existing CFG
tools in terms of node and edge coverage, especially for incomplete or
erroneous code. We also conducted an ablation experiment and confirmed the
effectiveness of AI chain design principles: Hierarchical Task Breakdown, Unit
Composition, and Mix of AI Units and Non-AI Units.Our work opens up new
possibilities for building foundational software engineering tools based on
LLMs, as opposed to traditional program analysis methods
Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
Foundation models, such as GPT-4, DALL-E have brought unprecedented AI
"operating system" effect and new forms of human-AI interaction, sparking a
wave of innovation in AI-native services, where natural language prompts serve
as executable "code" directly (prompt as executable code), eliminating the need
for programming language as an intermediary and opening up the door to personal
AI. Prompt Sapper has emerged in response, committed to support the development
of AI-native services by AI chain engineering. It creates a large language
model (LLM) empowered software engineering infrastructure for authoring AI
chains through human-AI collaborative intelligence, unleashing the AI
innovation potential of every individual, and forging a future where everyone
can be a master of AI innovation. This article will introduce the R\&D
motivation behind Prompt Sapper, along with its corresponding AI chain
engineering methodology and technical practices
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
There has been a recent surge in the study of generating recommendations
within the framework of causal inference, with the recommendation being treated
as a treatment. This approach enhances our understanding of how recommendations
influence user behaviour and allows for identification of the factors that
contribute to this impact. Many researchers in the field of causal inference
for recommender systems have focused on using propensity scores, which can
reduce bias but may also introduce additional variance. Other studies have
proposed the use of unbiased data from randomized controlled trials, though
this approach requires certain assumptions that may be difficult to satisfy in
practice. In this paper, we first explore the causality-aware interpretation of
recommendations and show that the underlying exposure mechanism can bias the
maximum likelihood estimation (MLE) of observational feedback. Given that
confounders may be inaccessible for measurement, we propose using contrastive
SSL to reduce exposure bias, specifically through the use of inverse propensity
scores and the expansion of the positive sample set. Based on theoretical
findings, we introduce a new contrastive counterfactual learning method (CCL)
that integrates three novel positive sampling strategies based on estimated
exposure probability or random counterfactual samples. Through extensive
experiments on two real-world datasets, we demonstrate that our CCL outperforms
the state-of-the-art methods.Comment: conferenc
Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain
API recommendation methods have evolved from literal and semantic keyword
matching to query expansion and query clarification. The latest query
clarification method is knowledge graph (KG)-based, but limitations include
out-of-vocabulary (OOV) failures and rigid question templates. To address these
limitations, we propose a novel knowledge-guided query clarification approach
for API recommendation that leverages a large language model (LLM) guided by
KG. We utilize the LLM as a neural knowledge base to overcome OOV failures,
generating fluent and appropriate clarification questions and options. We also
leverage the structured API knowledge and entity relationships stored in the KG
to filter out noise, and transfer the optimal clarification path from KG to the
LLM, increasing the efficiency of the clarification process. Our approach is
designed as an AI chain that consists of five steps, each handled by a separate
LLM call, to improve accuracy, efficiency, and fluency for query clarification
in API recommendation. We verify the usefulness of each unit in our AI chain,
which all received high scores close to a perfect 5. When compared to the
baselines, our approach shows a significant improvement in MRR, with a maximum
increase of 63.9% higher when the query statement is covered in KG and 37.2%
when it is not. Ablation experiments reveal that the guidance of knowledge in
the KG and the knowledge-guided pathfinding strategy are crucial for our
approach's performance, resulting in a 19.0% and 22.2% increase in MAP,
respectively. Our approach demonstrates a way to bridge the gap between KG and
LLM, effectively compensating for the strengths and weaknesses of both.Comment: Accepted on ASE'202
Unraveling the hidden function of a stabilizer in a precursor in improving hybrid perovskite film morphology for high efficiency solar cells
The morphology of the organometal trihalide perovskite (OTP) plays a critical role in the performance of solar cell devices. Nevertheless it has been frequently reported that the morphology of OTP films tends to be different in different laboratories even with the same film preparation procedure, which makes it very difficult to compare and understand the material and device physics. Here, we unravel a critical role of the H3PO2 stabilizer in HI, which has been largely ignored, in controlling the morphology of the perovskite films. The H3PO2 stabilizer in HI solution introduces MAH2PO2 impurities into the synthesized MAI (non-purified MAI) by reacting with methylamine (MA) aqueous solution. MAH2PO2 impurities can slow down the overall crystallization process of perovskite by forming an intermediate phase of Pb(H2PO2)2. Both MAH2PO2 and Pb(H2PO2)2 impede the fast reaction of PbI2 and MAI, resulting in highly uniform and smooth perovskite films with larger grain sizes. The recrystallization of non-purified MAI can remove the MAH2PO2 impurity and form purified MAI, which however results in rough and non-uniform perovskite films. Uniform and smooth perovskite films can also be obtained by directly adding artificially synthesized MAH2PO2 into the purified MAI precursor. This study also suggests Pb(H2PO2)2 to be a new precursor to formhigh quality perovskite films
Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference
APIs have intricate relations that can be described in text and represented
as knowledge graphs to aid software engineering tasks. Existing relation
extraction methods have limitations, such as limited API text corpus and
affected by the characteristics of the input text.To address these limitations,
we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural
knowledge base for API relation inference. This approach leverages the entire
Web used to pre-train LLMs as a knowledge base and is insensitive to the
context and complexity of input texts. To ensure accurate inference, we design
our analytic flow as an AI Chain with three AI modules: API FQN Parser, API
Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN
parser and API Relation Decider module are 0.81 and 0.83, respectively. Using
the generative capacity of the LLM and our approach's inference capability, we
achieve an average F1 value of 0.76 under the three datasets, significantly
higher than the state-of-the-art method's average F1 value of 0.40. Compared to
CoT-based method, our AI Chain design improves the inference reliability by
67%, and the AI-crowd-intelligence strategy enhances the robustness of our
approach by 26%
Therapeutic Rationale to Target Highly Expressed Aurora kinase A Conferring Poor Prognosis in Cholangiocarcinoma
Supply chain knowledge management:a literature review
This paper aims to contribute to the debate on the role of knowledge management in supply chain management by reviewing the published literature. A total of 58 selected referred journal articles were systematically analyzed. This review identifies various theoretical and methodological characteristics of the way in which knowledge management applications are proposed in the supply chain context. The review shows that little evidence exists of the positive relation between the use of IT solutions and firms’ performance. Some issues remain unexplored such as the problem of knowledge obsolescence in supply chain management. A deeper understanding of the knowledge accumulation process could give new insights. The paper concludes with some future directions for theory construction and empirical research
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