Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. This machine also complated with usb cable for pc connection.The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. This machine no need installation at wall and complex cabeling, only put it on desk and machine can operate directly. Solution P100 Fingerprint Attendance Machine that designed specially so can work as mobile cause allready integrated with internal litium battery inside and USB Charger for accept power input from USB cable only.
Software Fingerprint Solution P100 Download Software FingerprintCosmo Wolfe changed description of Download Software Fingerprint Solution X100c UPD Cosmo Wolfe on Download Software Fingerprint Solution X100c UPD SOFTWARE INCLUDE, Attendance logs can be downloaded to PC, so you can print any kind of attenandce reports, and also can be used for calculating your. Cosmo Wolfe changed description of Download Software Fingerprint Solution X100c UPD Cosmo Wolfe on Download Software Fingerprint Solution X100c UPD SOFTWARE INCLUDE, Attendance logs can be downloaded to PC, so you can print any kind of attenandce reports, and Actions. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs.These tools not only function as the basis of human language imitation but also play a key role for API offerings in AI applications. The use of artificial intelligence for human-computer interaction in voice assistant machines-related tools have flourished, such as Tesla’s NoA, Apple’s Siri, Amazon Echo and Alexa, and Google Home. Accordingly, research about human-computer interaction is meant to imitate human behavior, especially natural language representation and interpretation in the voice assistant machine. Since then, research in artificial intelligence has been increasing again. Link driverAlpha Go was developed by Google DeepMind in London in 2014, and it defeated all other Go masters. SUDAH INCLUDE SOFTWARE, Data Absensi dapat ditarik ke komputer, lalu mencetak berbagai macam laporan kehadiran. ![]() Famous open-source pretrained language models, such as ELMo, BERT, and GPT-2 , can implement the best level of Natural Language Processing (NLP) tasks, and they have the ability required for huge hierarchical models. The use of recently developed language models with large-scale data and huge computing power can help solve various applications of human natural language. Language model is a technology that allows machines to understand and predict human language. At present, the most fluent open source natural language model is the Generative Pre-trained Transformer 2 (GPT-2) , which is a natural language simulation machine developed by using OpenAI. Regarding the technology involved in the improvement method, a complete and quickly searchable semantic database using MariaDB is constructed with the Natural Language Toolkit (NLTK) model of sentence segmentation to provide correct answers to users. The natural language processing involves understanding and generating. Second, how to generate high-performance programs through the code transform model to replace the existing programs inside the machine to improve execution performance. First, how to construct a complete semantic database that can implement data retrieval quickly and respond correctly. Nevertheless, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs produced by GPT-2, which guarantee that the best one can be found within them. Python combined with Hadoop Streaming to provide big data processing and distributed storage in Hadoop, and it can also be used with PySpark to provide big data analytics in Spark.There is so far no way to infer how many programs generated from GPT-2 can guarantee a pass ratio over 90% without which we cannot find the best one among them. In this study, Python provides the run-time environment for the aforementioned tools, namely, MariaDB, NLTK, GPT-2, and Tensorlfow. In Section 2, related work in word segmentation processing and language generation model will be described. In addition, we present cosine text similarity algorithm Simhash to check the code similarity of generated programs and example programs and use the longest common subsequence (LCS) to verify the conformity between the execution results of generated programs and example programs.The following paragraphs of this paper are arranged as follows. In order to improve the efficiency of keyword retrieval, both the parameters of filtering redundant keywords and adding new keywords are used to optimize keyword-search in the semantic database in order to improve the hit rate of keywords from the database. Connecting xbox one controller to dolphin emulator macIn order to respond to people, smart speakers and the technology of natural language generation (NLG) for suggestions, content determination, conversation planning, sentence aggregation, vocabulary, citation expression generation, language experiment, and descriptions of basic NLG task have been developed. Then, natural language understanding (NLU) is used to translate these words into meanings. The audio system relies on a set of complex AI technologies that use automatic speech recognition (ASR) to receive sound waves and to convert them into words. Related WorkA particular voice assistant machine, Amazon Alexa , is a smart assistant and Echo smart speaker developed in recent years. Finally, we drew a brief conclusion in Section 5. The experimental results and discussion is given in Section 4. (2011) used XML and XSLT technology to generate a web page code. This would be a great progress in the application of AI, and the audio-text conversion technology would further apply in the sound-controlled related products, such as drones, robots, and flying iron men.There is not much literature on source-code to source-code transform model. In contrast, a high-performing new program can be generated through the code transform model, and it will operate faster than the original one. As a result, the program execution speed may be too slow to get the job done, and there may still be deficiencies when running the program in real-time. In reality, people are often pressurized to complete programming or modify the codes of a program before the deadline or the challenge of improving the execution of a program. No matter whether it is using Java-Codetool or CodeGeneration, the number of code lines is not reduced. The experimental results show that the average time to generate a code line in a program using Java-Codetool is about 0.17 seconds, and CodeGeneration takes 0.15 seconds. They took the Binary tree traversal program as an example and divided the program into 4 parts to produce newly generated source-code programs individually. (2020) employed Java code transform models, Java-Codetool, and CodeGeneration to produce newly generated java programs and evaluate their performance. However, they did not provide any data to show how the results were generated. Word Segmentation Processing-NLTKNatural Language Processing (NLP) is regarded as a branch of AI and linguistics. In order to realize the “applying code transform model to newly generated program for improving execution performance”, this study will use the following key technologies: Anaconda (Data Science Platform with Virtual Environment Conda), Tensorflow (Dataflow and Differentiable Programming), NLTK (English Text Segmentation), GPT-2 (Text Generating Model), and Simhash (Cosine Text Similarity Algorithm), etc., to achieve the goal of this research. Despite this, the system can generate similar programs instantly to greatly reduce the possibility of errors or incomplete programs with GPT-2. In this paper, the proposed approach will present the code similarity check and make sure the conformity of the execution results between generated programs and sample programs that resulted in the credibility and validity of the findings in this study.Uncertainties will exist depending on the situation. However, it did not show further information about the execution results of their generated programs.
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