111 research outputs found
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From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification
The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based Mary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQMDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost
Energy efficient tag identification algorithms for RFID: survey, motivation and new design
RFID is widely applied in massive tag based applications, thus effective anti-collision algorithms to reduce communication overhead are of great importance to RFID in achieving energy and time efficiency. Existing MAC algorithms are primarily focusing on improving system throughput or reducing total identification time. However, with the advancement of embedded systems and mobile applications, the energy consumption aspect is increasingly important and should be considered in the new design. In this article, we start with a comprehensive review and analysis of the state-of-the-art anti-collision algorithms. Based on our existing works, we further discuss a novel design of anti-collision algorithm and show its effectiveness in achieving energy efficiency for the RFID system using EPCglobal C1 Gen2 UHF standard
TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design
High-quality instruction-tuning data is critical to improving LLM
capabilities. Existing data collection methods are limited by unrealistic
manual labeling costs or by the hallucination of relying solely on LLM
generation. To address the problems, this paper presents a scalable method to
automatically collect high-quality instructional adaptation data by training
language models to automatically design tasks based on human-written texts.
Intuitively, human-written text helps to help the model attenuate illusions
during the generation of tasks. Unlike instruction back-translation-based
methods that directly take the given text as a response, we require the model
to generate the \textit{instruction}, \textit{input}, and \textit{output}
simultaneously to filter the noise. The results of the automated and manual
evaluation experiments demonstrate the quality of our dataset.Comment: Work in progres
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
Conventional text-to-SQL studies are limited to a single task with a
fixed-size training and test set. When confronted with a stream of tasks common
in real-world applications, existing methods struggle with the problems of
insufficient supervised data and high retraining costs. The former tends to
cause overfitting on unseen databases for the new task, while the latter makes
a full review of instances from past tasks impractical for the model, resulting
in forgetting of learned SQL structures and database schemas. To address the
problems, this paper proposes integrating semi-supervised learning (SSL) and
continual learning (CL) in a stream of text-to-SQL tasks and offers two
promising solutions in turn. The first solution Vanilla is to perform
self-training, augmenting the supervised training data with predicted
pseudo-labeled instances of the current task, while replacing the full volume
retraining with episodic memory replay to balance the training efficiency with
the performance of previous tasks. The improved solution SFNet takes advantage
of the intrinsic connection between CL and SSL. It uses in-memory past
information to help current SSL, while adding high-quality pseudo instances in
memory to improve future replay. The experiments on two datasets shows that
SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple
metrics.Comment: Accepted by AAAI-202
Evaluation of ChatGPT as a Question Answering System for Answering Complex Questions
ChatGPT is a powerful large language model (LLM) that has made remarkable
progress in natural language understanding. Nevertheless, the performance and
limitations of the model still need to be extensively evaluated. As ChatGPT
covers resources such as Wikipedia and supports natural language question
answering, it has garnered attention as a potential replacement for traditional
knowledge based question answering (KBQA) models. Complex question answering is
a challenge task of KBQA, which comprehensively tests the ability of models in
semantic parsing and reasoning. To assess the performance of ChatGPT as a
question answering system (QAS) using its own knowledge, we present a framework
that evaluates its ability to answer complex questions. Our approach involves
categorizing the potential features of complex questions and describing each
test question with multiple labels to identify combinatorial reasoning.
Following the black-box testing specifications of CheckList proposed by Ribeiro
et.al, we develop an evaluation method to measure the functionality and
reliability of ChatGPT in reasoning for answering complex questions. We use the
proposed framework to evaluate the performance of ChatGPT in question answering
on 8 real-world KB-based CQA datasets, including 6 English and 2 multilingual
datasets, with a total of approximately 190,000 test cases. We compare the
evaluation results of ChatGPT, GPT-3.5, GPT-3, and FLAN-T5 to identify common
long-term problems in LLMs. The dataset and code are available at
https://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-ChatGPT
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A partitioning approach to RFID identification
Radio-frequency identification (RFID) is a major enabler of Internet of Things (IoT), and has been widely applied in tag-intensive environments. Tag collision arbitration is considered as a crucial issue of such RFID system. To enhance the reading performance of RFID, numerous anti-collision algorithms have been presented in previous literatures. However, most of them suffer from the slot efficiency bottleneck of 0.368. In this paper, we revisit the performance of tag identification in Aloha-based RFID anti-collision approaches from the perspective of time efficiency. Based on comprehensive reviews and analysis of the existing algorithms, a novel partitioning approach is proposed to maximize identification performance in framed slotted Aloha based UHF RFID systems. In the proposed approach, the tag set is divided into many groups which only contains a few tags, and then each group is identified in sequence. Benefiting from the optimal partition, the proposed algorithm can achieve a significant performance improvement. Simulation results supplemented by prototyping tests show that the proposed solution achieves an asymptotical slot efficiency up to 0.4348, outperforming the existing UHF RFID solutions
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A group-based binary splitting algorithm For UHF RFID anti-collision systems
Identiļ¬cation efļ¬ciency is a key performance metrics to evaluate the ultra high frequency(UHF) based radio frequency identiļ¬cation (RFID) systems. In order to solve the tag collision problem and improve the identiļ¬cation rate in large scale networks, we propose a collision arbitration strategy termed as group-based binary splitting algorithm (GBSA), which is an integration of an efļ¬cient tag cardinality estimation method, an optimal grouping strategy and a modiļ¬ed binary splitting. In GBSA, tags are properly divided into multiple subsets according to the tag cardinality estimation and the optimal grouping strategy. In case that multiple tags fall into a same time slot and form a subset, the modiļ¬ed binary splitting strategy will be applied while the rest tags are waiting in the queue and will be identiļ¬ed in the following slots. To evaluate its performance, we ļ¬rst derive the closed-form expression of system throughput for GBSA. Through the theoretical analysis, the optimal grouping factor is further determined. Extensive simulation results supplemented by prototyping tests indicate that the system throughput of our proposed algorithm can reach as much as 0.4835, outperforming the existing anti-collision algorithms for UHF RFID systems
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