Reviewerly/exHarmony huggingface dataset:
TAUCHI-GPT: Leveraging GPT-4 to create a Multimodal Open-Source Research AI tool

In the last few year advances in deep learning and artificial intelligence have made it possible to generate high-quality text, audio, and visual content automatically for a wide range of application areas including research and education. However, designing and customizing an effective R&amp;D tool capable of providing necessary tool-specific output, and breaking down complex research tasks requires a great deal of expertise and effort, and is often a time-consuming and expensive process. Using existing Generative Pre-trained Transformers (GPT) and foundational models, it is now possible to leverage the Large Language Model GPTs already trained on specific datasets to be effective in common research and development workflow. In this paper, we develop and test a customized version of autonomous pretrained generative transformer which is an experimental open-source project built on top of GPT-4 language model that chains together LLM "thoughts", to autonomously achieve and regress towards specifics goals. Our implementation, referred to as TAUCHI-GPT, which uses an automated approach to text generation that leverages deep learning and output reflection to create high-quality text, visual and auditory output, achieve common research and development tasks. TAUCHI-GPT is based on the GPT-4 architecture and connects to Stable Diffusion and ElevenLabs to input and output complex multimodal streams through chain prompting. Moreover, using the Google Search API, TAUCHI-GPT can also scrap online repositories to understand, learn and deconstruct complex research tasks, identify relevant information, and plan appropriate courses of action by implementing a chain of thought (CoT).
Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the spatiotemporal models and the transformer models according to the architecture. And this paper briefly analyzes the characteristics of the four models and introduces the accuracy of various algorithms in common data sets. Then, from the perspective of tasks to be completed, action detection is further divided into temporal action detection and spatiotemporal action detection, and commonly used datasets are introduced. From the perspectives of the two-stage method and one-stage method, various algorithms of temporal action detection are reviewed, and the various algorithms of spatiotemporal action detection are summarized in detail. Finally, the relationship between different parts of action recognition and detection is discussed, the difficulties faced by the current research are summarized in detail, and future development was prospected.
A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition (ASR) technology in the Air Traffic Control (ATC) field. This paper presents a novel cascaded model architecture, namely Conformer-CTC/Attention-T5 (CCAT), to build a highly accurate and robust ATC speech recognition model. To tackle the challenges posed by noise and fast speech rate in ATC, the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms. On the decoding side, the Attention mechanism is integrated to facilitate precise alignment between input features and output characters. The Text-To-Text Transfer Transformer (T5) language model is also introduced to handle particular pronunciations and code-mixing issues, providing more accurate and concise textual output for downstream tasks. To enhance the model’s robustness, transfer learning and data augmentation techniques are utilized in the training strategy. The model’s performance is optimized by performing hyperparameter tunings, such as adjusting the number of attention heads, encoder layers, and the weights of the loss function. The experimental results demonstrate the significant contributions of data augmentation, hyperparameter tuning, and error correction models to the overall model performance. On the Our ATC Corpus dataset, the proposed model achieves a Character Error Rate (CER) of 3.44%, representing a 3.64% improvement compared to the baseline model. Moreover, the effectiveness of the proposed model is validated on two publicly available datasets. On the AISHELL-1 dataset, the CCAT model achieves a CER of 3.42%, showcasing a 1.23% improvement over the baseline model. Similarly, on the LibriSpeech dataset, the CCAT model achieves a Word Error Rate (WER) of 5.27%, demonstrating a performance improvement of 7.67% compared to the baseline model. Additionally, this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems. In robustness evaluation experiments based on this criterion, the proposed model demonstrates a performance improvement of 22% compared to the baseline model.
Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1 × 1 convolution and replaces the 7 × 7 convolution structure with the asymmetric structure of 1 × 7 convolution, 7 × 1 convolution, and 7 × 7 convolution in parallel. The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks, and the convolutional layers in each dense block are connected, thus improving the feature transitivity, enhancing the network’s ability to extract features, thus improving the detection accuracy. Two representative datasets, Multiple Cameras Fall dataset (MCF), and Nanyang Technological University Red Green Blue + Depth Action Recognition dataset (NTU RGB + D), are selected for our experiments, among which NTU RGB + D has two evaluation benchmarks. The results show that the proposed model is superior to the current fall detection models. The accuracy of this network on the MCF dataset is 96.3%, and the accuracies on the two evaluation benchmarks of the NTU RGB + D dataset are 85.6% and 93.5%, respectively.
The Portal Galleries: Researching Portals in Fiction from the 19th Century to the Present

Abstract The notion of portals to alternative multispaces where normalcy is subverted into magical situations has been a feature of fiction for centuries. Architects and educators Lara Lesmes and Fredrik Hellberg have catalogued some of these fictive realms and mechanisms, and developed their own augmented‐reality portal to show the results of their research.
Money as an Interface

research-article Share on Money as an Interface Author: Belén Barros Pena City, University of London City, University of LondonView Profile Authors Info & Claims InteractionsVolume 30Issue 6November - December 2023pp 42–46https://doi.org/10.1145/3627702Published:01 November 2023Publication History 0citation225DownloadsMetricsTotal Citations0Total Downloads225Last 12 Months225Last 6 weeks185 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Low-Rank Representation Based Model for Sar Image Denoising and Edge-Preserving

Synthetic aperture radar imaging is a powerful remote sensing technology, but its images are often degraded by speckle noise, reducing quality. In recent years, speckle reduction has become a hot topic in SAR research, with various methods emerging. However, traditional low-rank SAR denoising causes edge blurring and loss of important features. To address this, we propose a new approach using prior knowledge to distinguish noise pixels from edges. By separating low-rank residuals and effectively extracting edge information, our method outperforms others, generating noise-suppressed SAR images while retaining features. Experiments demonstrate the effectiveness of our approach.
3D Object Detection Method Based on CA Sampling and Local Attention Feature Encoding

The utilization of LiDAR point cloud in various applications, such as autonomous driving and intelligent transportation, has gained significant attention and become a research hotspot. However, existing 3D object detection methods face two main challenges. (1) random or farthest point sampling can lead to a loss of foreground points and missed detections, particularly for small objects. (2) the sparsity of LiDAR point clouds causes the object point clouds to be non-uniformly distributed. In this paper, we present a novel method for 3D object detection. Our approach is based on the PV-RCNN network architecture and incorporates Centroid-aware (CA) Sampling and Local Attention Feature Encoding techniques. The voxel branch in feature extraction uses the Focals Conv module to predict the importance of voxel features at different locations and enhances the representation of local spatial structure by attention convolution. The key point branch uses CA Sampling to obtain key points. We calculate mask weights based on the position relationship between each point and the 3D bounding boxes, and the K highest scoring points are retained as key points. We further improve the local feature expression capability by aggregating each key point with the original point cloud and voxel features in the neighborhood using the PointNet++ network. Finally, we combine the collocated features and feed them into the classification and regression network of PV-RCNN to complete the detection process. Our simulation experiments show that the detection performance of our proposed method outperforms the PV-RCNN on the KITTI dataset.
Modified Deep Neural Network for Object Recognition

Object recognition has gained significance due to the rise in CCTV surveillance and the need for automated detection of objects or activities in images and videos. Lightweight process frameworks are in demand for sensor networks. While Convolutional Neural Networks (CNNs) are widely used in computer vision, many existing architectures are specialized. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), enhancing CNNs with dimension-wise selection of kernels for improved performance. The DBGC offers flexibility for height, width, and depth kernels and can be applied to different dimension combinations. A key feature is the dimension selector block. Unoptimized kernel dimensions reduce computational operations and accuracy, while semi-optimized ones maintain accuracy with fewer operations. Optimized dimensions provide 5-6% higher accuracy and reduced operations. This work addresses the challenge of generic architecture in object recognition research.
Educational Opportunities

Journal AWWAVolume 115, Issue 9 p. 84-84 Educational Opportunities Educational Opportunities First published: 01 November 2023 https://doi.org/10.1002/awwa.2187Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume115, Issue9November 2023Pages 84-84 RelatedInformation
What makes deceptive online reviews? A linguistic analysis perspective

Abstract With the rapid development of e-commerce, online reviews have become an important information source for consumers and e-commerce businesses. While the negative impact of deceptive online reviews has been well recognized, more research has to be done to help understand the linguistic manifestations of deceptive online reviews in order to help identify deceptive reviews and help increase the value and sustainability of e-commerce businesses. This study explores the linguistic manifestations of deceptive online reviews based on the reality monitoring theory, and then uses the data from Amazon.com online product reviews to examine perceptual cues, affective cues, detail cues, relevance cues, and cognitive cues of various deceptive online reviews. The results show that reviews for emotional catharsis are more extreme with affective cues, while perfunctory reviews often lack details with fewer prepositions and adjectives. In addition, deceptive reviews often lack relevance cues when these reviews are made to obtain the rewards provided by the vendors while paid posters tend to use more cognitive cues in deceptive reviews. Moreover, deceptive online reviews under all motives often lack perceptual cues. These findings provide a deeper understanding of the linguistic manifestations of deceptive online reviews and provide significant managerial implications for e-commerce businesses to employ high-quality online reviews for sustainable growth.
Compression and Decompression Using Deep Neural Network

An auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. It is tested by tensorflow and mnist dataset. The abstract network is like LeNet-5. The concrete network is the inverse of the abstract network. A picture can change to label that is compression, then change it back from label that is decompression. So compression and decompression can be realized by the autoencoder. Through test, the absolute function can do generation task well, while the leaky relu function can do classification task well. Lossy compression can be achieved by abstract network and concrete network with absolute function. With one-hot encoding, the compression ratio is 5.1% when decompression quality is good. With binary encoding, the compression ratio is 2% when decompression quality is ok. The mean PNSR of five pictures is 19.48 dB. When jump connection and negative feedback are used, the decompression performance is good. The mean PNSR of five pictures is 29.86 dB. The compression ratio is 56.1%.
Evolutionary Machine Learning and Games

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.
Comparative Forecasting of Key Fisheries Related Indicators in Gyeongnam and Jeonnam

The purpose of this study is to predict key indicators that form the basis of the fishing industry in the Gyeongnam and Jeonnam regions, which represent the Korean fishing industry, and to forecast the next six years for these regions. For this purpose, the main indicators used in the analysis are the total volume and value of fisheries products in individual regions, the inshore and offshore production volume and value, aquaculture production volume and value, number of fishing vessels, horsepower, and tonnage. The ETS exponential smoothing model and the ARIMA model were used in this analysis, and the prediction performance of these models was also compared using the MAPE indicator based on the actual and estimated values of the out-of-sample period. As a result of the analysis, the ARIMA model was found to have better predictive power than the ETS exponential smoothing model, except for the inshore and offshore fishery production in Gyeongnam and the inshore and offshore production value in Jeollanam-do. In addition, Gyeongnam
Executing Quality Management Tools to Enhance Customer’s Journey at a Clothes Laundry Firm

Purpose: This paper focuses on assessing and enhancing the customer’s journey at a laundry company in Saudi Arabia. This begins from the moment the customer arrives at the car park, including the process of receiving the services and subsequently making payment. Design, Methodology, Approach: A mixed-method approach was employed and qualitative data were gathered from focus group interviews and individual interviews. Quantitative data were gathered via the survey. Quality Management Tools were used to develop the action plan and offer conclusions and recommendations that would enhance the customer’s journey and improve satisfaction. Findings: Issues relating to service quality are categorized into five types: promotions, human resources, services, detergent products, and facilities. House of Quality represents the highest 15 prioritized solutions. These recommended solutions' relative weights range between 4 to 9%. The use of these tools highlights areas for improvement and the root causes of each issue. The voice of the customer and the voice of the employees are key sources to obtain valuable input. This research proffers step-by-step practical guideline for service organizations on how they can apply the quality management tools in their business, which will help them identify issues and continue improving. Its implications to academia merge the ServQual theory and QM in customer services and satisfactions. Originality/Value: This study will add value to the literature since there are limited studies employing the seven Quality Management Tools to enhance customer satisfaction and improve their journey. The Saudi laundry market specifically has a dearth of this type of study. The leadership of an organisation in the laundry sector is established through the implementation of tailored quality solutions, the prioritisation of customer satisfaction, and the enhancement of operational efficiency.
Fog Forensics: A Comprehensive Review of Forensic Models for Fog Computing Environment

Numerous potential social advantages are offered by fog computing, including personalized healthcare, smart cities, agri technology, automated transportation, consumer IoT, and many more. Ambient computing at previously unfathomable scales is made possible by the extremely dynamic and complex nature of fog computing and its low latency communication networks connecting sensors, devices, and actuators. The need to look for digital forensic methods that may effectively be used to solve computer-related crimes utilizing IoT devices is being driven by the rise in IoT devices. Fog computing adds greater threats to privacy and security as it is becoming challenging given the increasing number of linked devices. The existing forensics models are not sufficient to handle data from the fog cloud. In this paper, we present a thorough review of the existing state-of-the-art forensic models that can be applied to fog cloud environment and this work can further be used to promote extensive research and development of fog forensic models.
Data Adaptive Semantic Communication Systems for Intelligent Tasks and Image Transmission

In this paper, we consider an end-to-end semantic communication system for compressed image wireless transmission. To economize the communication bandwidth, and enhance the communication reliability as well as the intelligence of the receiver, two networks are designed for image transmission and intelligent tasks. In particular, two simple linear layers are used to extract the feature of the image and recover the image, taking into account the sparsity of the image in a specific linear space. The trained encoder is deployed at the transmitter, while the trained decoder and the classifier are deployed at the receiver. To adapt to new communication data, the proposed networks are trained in a meta-learning framework. A few samples of new data are fed into the trained network to calculate new model parameters, which are fed back to the transmitter for updating the network of the encoder. Experimental results show that the proposed system has superior performance in terms of image compression transmission over fading channels compared with the existing semantic communication systems.
DSQA-LLM: Domain-Specific Intelligent Question Answering Based on Large Language Model

Question Answering (QA) is crucial for humans to access vast knowledge bases, but there is a lack of attention towards representing raw, unstructured questions and answers in specific fields. Additionally, the efficiency of finding candidate questions based on the trigger question and the generation of reasonable answers have been neglected. In this paper, we introduce Domain Specific Question Answering Language Model (DSQA-LLM), a framework that delivers informative answers within a specific domain. We utilize techniques like question classification, information retrieval, and answer generation. We enhance efficiency and accuracy through the integration of XLNET for question classification and a novel similarity searching method using Sentence-T5. Furthermore, the powerful GPT-3.5-turbo is employed for generating coherent answers. We implemented DSQA-LLM and curated a dataset of 127,840 question-answer pairs. Empirical experiments conducted on real-world questions confirm the effectiveness of our QA system.