Using Word2Vec and N-grams to Generate Poetic Texts

This research attempts to create poetic texts in the Shakespearean style. Many available technologies can create artworks based on styles of well-known artists. However, it is difficult for the generative models to create texts within the style of a particular author. This study aims to produce sentences in the style of Shakespeare that contain metaphorical meanings without quoting his works. We have trained statistical n-gram models on the complete works of Shakespeare and then used the model to create Shakespearean text. We noticed that, in some cases, the n-gram models will construct sentences that are copied from Shakespeare’s works. We then trained a neural-AI Word2Vec model word embedding to “paraphrase” words with others that have similar semantics (and thus preventing plagiarism). The initial results include some adequate sentences and some with semantic errors. There are also many ungrammatical utterances. We are focusing on substituting words with correct part-of-speech and screening out ungrammatical results. We also incorporated the Glove embedding model, which is trained on a large corpus of modern texts, to add modern words and themes into the Shakespeare-styled generated results. This research will give insights on how the models can generate texts that are more artistic

Digital Technology-driven Business Model Innovations: A Bibliometric Analysis

With the advent of the data age, digital technology has been widely used in business model innovation. To understand the current research situation in the field of digital technology-driven business model innovation and reveal the knowledge structure, research hotspots, and development trends in this research field, this paper adopts statistical analysis, co-citation analysis, cluster analysis and other methods to carry out bibliometric analysis and knowledge mapping on the relevant literature included in the Web of Science database. The research results show that customer relationship management, digital economy and financial service system, sustainable development and digital service innovation, and the competition and cooperation mechanism of enterprises are hot topics in this field. Moreover, digital platform, firm performance, and value creation are the main research directions in the future

Research on Online Moisture Detector in Grain Drying Process Based on V/F Conversion

An online resistance grain moisture detector is designed, based on the model of the relationship between measurement frequency and grain moisture and the nonlinear correction method of temperature. The detector consists of lower computer, the core function of which is the sensing of grain resistance values which is based on V/F conversion, and upper computer, the core function of which is the conversion of moisture and frequency and the nonlinear correction of temperature. The performance of the online moisture detector is tested in a self-designed experimental system; the test and analysis results indicate that the precision and stability of the detector can reach the level of the similar products, which can be still improved

PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection

With careful manipulation, malicious agents can reverse engineer private information encoded in pre-trained language models. Security concerns motivate the development of quantum pre-training. In this work, we propose a highly portable quantum language model (PQLM) that can easily transmit information to downstream tasks on classical machines. The framework consists of a cloud PQLM built with random Variational Quantum Classifiers (VQC) and local models for downstream applications. We demonstrate the ad hoc portability of the quantum model by extracting only the word embeddings and effectively applying them to downstream tasks on classical machines. Our PQLM exhibits comparable performance to its classical counterpart on both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy) metrics. We also perform ablation studies on the factors affecting PQLM performance to analyze model stability. Our work establishes a theoretical foundation for a portable quantum pre-trained language model that could be trained on private data and made available for public use with privacy protection guarantees.Comment: 5 pages, 3 figures, 3 table

A New Kind of Sub-pixel Interpolation Filtering Algorithm and Hardware Structural Design

Considering the problems of high complexity of sub-pixel interpolation operation and large visitor volume of storage in H.264/AVC standard, a kind of sub-pixel interpolation operation is put forward with changeable filter coefficient and unchangeable coefficient sum. According to the video image, filter coefficient can be confirmed. Based on the algorithm, a kind of 1/4 pixel precision interpolative hardware structural design. It is indicated by the performance analysis and filter structure that the structure is able to calculate pixel interpolation at different positions in a certain clock period with the characteristics of small area and fast speed. It is indicated by the result of experiment that compared with H.264 standard, new algorithm is able to reduce 18 % space complexity, improve noise ratio of peak, reduce bit rate and improve the performance of coding

Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex

While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored. We propose an artificial neural network dubbed VISION, an acronym for "Visual Interface System for Imaging Output of Neural activity," to mimic the human brain and show how it can foster neuroscientific inquiries. Using visual and contextual inputs, this multimodal model predicts the brain's functional magnetic resonance imaging (fMRI) scan response to natural images. VISION successfully predicts human hemodynamic responses as fMRI voxel values to visual inputs with an accuracy exceeding state-of-the-art performance by 45%. We further probe the trained networks to reveal representational biases in different visual areas, generate experimentally testable hypotheses, and formulate an interpretable metric to associate these hypotheses with cortical functions. With both a model and evaluation metric, the cost and time burdens associated with designing and implementing functional analysis on the visual cortex could be reduced. Our work suggests that the evolution of computational models may shed light on our fundamental understanding of the visual cortex and provide a viable approach toward reliable brain-machine interfaces