Reviewerly/exHarmony huggingface dataset:
Noise Floor Characterization in Accelerometers for Earthquake Monitoring

Earthquake prediction and monitoring are increasingly needed for humans because of their fatal effects. Given the unpredictable nature of these phenomena, early detection of possible damaging seismic waves is crucial and, therefore, has been an increasingly main focus of research. Timely detection of earthquakes comes through seismic stations in addition to algorithms. These stations contain extremely expensive instrumentation, which limits their widespread use. Nowadays, however, many sensors have been designed to be capable of making the same measurements as seismographs at a significantly lower price. In order to verify the applicability of MEMS accelerometers in structural building monitoring and earthquake prediction, it is mandatory to characterize their noise floor. Given the extensive comparison of the strengths and weaknesses of the two accelerometers already discussed by many authors, this paper will compare their performances for earthquake detection applications. A noise floor characterization of these sensors is presented. The objective is to perform a prior metrological evaluation of these instruments, comparing low-cost MEMS-based and analog accelerometers used to date. A comparison of noise floors using FFT and Allan Variance will be reported. The good results show these instruments' applicability in the field, paving the way for their more widespread distribution.
Fall Detection System for Elderly (FDS-E) using Low-cost Camera Based on LSTM and OpenPose

Recently, many countries have been dealing with an increasing amount of elderly who are living alone. Unfortunately, fall incidents in the elderly tend to happen, and without proper dependable care would lead to fatal injuries. The elderly must be closely monitored under constant observation. Accidental falls are a leading cause of injury, mortality, and mobility limitations in the elderly. The expenses of national healthcare systems are also significantly impacted by accidental falls. These findings highlight the importance of investing heavily in the study and advancement of fall detection and intervention technologies. It is essential to recognize them early and provide fast assistance by developing technologies that can simultaneously improve the standard and the safety of the elderly's living environment. In this paper, we present a computer vision algorithm for falling incident detection using a low-cost camera and deep learning model. Ultimately, this algorithm can be used for the cases of elderly, in which falling risk is high and fatal. Furthermore, one self-developed dataset was used to validate the suggested approach experimentally. We used an OpenPose-based feature using a low-cost camera input and then classified each event or activity as either fall or non-fall. Classification tasks are carried out by Long Short-Term Memory (LSTM) with multiple validation metrics. The LSTM in this work is also optimized using Bayesian method. Finally, our approach to recognize falls has accuracy of 99.5%, which is distinct from other past works.
An Affordable EOG-based Application for Eye Dystonia Evaluation

This paper evaluates the risk of ocular dystonia-a condition marked by excessive blinking-using electrooculography. A commercial bioamplifier is employed to capture the electrical activity of the eyes using dispensed surface electrodes. The continuous wavelet transform of the electrooculogram was estimated to identify the features related to involuntary eye-blinking behavior and make the classification. The signal processing is integrated into a novel application with a simple graphical user interface oriented to be used by physicians. The performance is evaluated using multiple evaluation measures. Results show that the proposed method succeeded in identifying an abnormal frequency of blinks with respective accuracy, precision, sensitivity, and specificity scores of 98.46%,96.51%,99.13%, and 96.41%.
An ML pipeline for real-time activity detection on low computational power devices for metaverse applications

This paper presents our recent work on real-time human activity detection based on the mediapipe pipeline and machine learning algorithms. A single webcam was used to capture the subject images and the data was recorded in a csv file. These CSV files were used to training and testing machine learning (ML) modes. The proposed system can detect human activities including running, jumping, squatting, bending to the left or right, and standing still. An open source framework named Mediapipe has been used to identify body makers which in result used for body pose. We tried hard coded approach and machine learning based approaches. Four models including random forest (RF), K-nearest neighbor (KNN), neural networks (NN) and convolution neural network (CNN) were tested. NN and CNN perfomed best with NN obtaining maximum accuracy of 99.76% and CNN obtaining accuracy of 100% on the test data sets. The proposed solution provides a cheap and affordable solution for home based exergaming ang rehabilitation.
Face Detection in Thermal Images with Improved Spatial Precision and Temporal Stability

Thermal video can be used as a privacy-preserving and non-contact sensor for long-term health monitoring including respiratory activity. Face detection from thermal images is required to define the region of interest for automated respiration monitoring. In this study, we focus on thermal face detection using deep learning-based methods and transfer learning. First, YOLOv7, YOLOv7-tiny, and Detector Transformer (DeTr) object detection models were trained on an open thermal image dataset of faces. The weights from the pretrained models were transferred to a new model that was trained on our own target dataset. Results showed that transfer learning resulted in improved intersection-over-union (IoU) face detection performance. Moving beyond face detection in a single frame, we evaluated the stability of the trained face detection model with regard to the time-consistency of the detected bounding boxes in thermal videos. The DeTr model showed higher performance with 0.812 IoU and more stable predicted bounding boxes compared to YOLOv7 and YOLOv7-tiny. The proposed methods were also evaluated with regard to model size, as it pertains to viable deployment using edge computing, as part of a complete respiration rate estimation pipeline.
Classification and Clustering for predicting breathalyzer failures

This paper reports the use of Machine Learning, classification and clustering to predict failures of breathalyzers used in traffic enforcement. Classification is based on training with data from periodic verifications, where errors and measurement deviations are analyzed against a certified reference material. Clustering finds groups based on similarities, according to the responses of electrochemical cells built into the instruments. Both approaches are valuable tools for monitoring breathalyzer wear, and their applications reduce the occurrence of false-positives and false-negatives in field sobriety checkpoints. The choice of one of the two or a combination of both can be applied by regulatory bodies or companies that carry out repairs, in order to provide greater assurance of quality in the use of the breathalyzers.
Anomaly Detection on Industrial Electrical Systems using Deep Learning

The recent development and spread of artificial intelligence-based techniques, particularly deep learning algorithms, have made it possible to model phenomena that were previously impossible to handle. Furthermore, the development of the Big Data paradigm is rapidly leading toward new research frontiers in predicting and classifying one-dimensional signals. Anomaly detection plays a crucial role in the various areas that gain from the introduction of these methodologies. This extremely diverse field detects anomalies in both time series and image data. Anomaly detection applications include the detection of failures of grid-connected machinery in industrial environments. The objective of this study was to propose a fault detection methodology based on deep learning, specifically using convolutional autoencoders, using as few features as possible, specifically the current intensity in one of the three phases of an industrial plant. The results showed a high capability of the methodology to detect faults while generating a minimum number of false positives, paving the way for optimizations of the same and online deployment.
Reference-frame-independent measurement device-independent quantum key distribution with heralded single-photon sources

Quantum cryptography has a strong security that classical cryptography cannot match, and quantum key distribution (QKD) is the most important method of quantum cryptography. Reference-frame-independent measurement-device-independent QKD (RFI-MDI-QKD) protocol can avoid the drift of the reference frame and all side channels of measurement devices. However, RFI MDI-QKD usually employs a weak coherent state (WCS) as the light source, which limits the transmission distance of secure keys. Here, by using heralded single-photon sources, we investigate an improved decoy-state RFI-MDI-QKD scheme. We perform numerical simulations for the scheme under different scenarios, such as considering the finite size and different drift angles. Compared with the scheme with WCS, our scheme can significantly improve the transmission distance of RFI-MDI-QKD.
Improving Image Compression Performance by Spatial-Channel Context Adaptive Model

The significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. The results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression.
CIPTA: Contrastive-based Iterative Prompt-tuning Using Text Annotation from Large Language Models

In recent years, public opinion analysis has become increasingly important due to the widespread use of social media platforms and the growing influence of online information on public security. Prompt tuning, a typical few-shot learning method, ensures that the model quickly adapts to opinion analysis with different classification rules. However, existing prompt tuning for opinion analysis cannot guarantee the effectiveness of the model in zero-shot or one-shot cases. In this study, we propose the Contrastive-based Iterative Prompt-tuning method using Text-Annotation from Large Language Models (LLMs), CIPTA, for low-resource public opinion analysis. Specifically, with a small amount of manually labeled data, CIPTA leverages the knowledge from LLMs to text annotation and utilizes unsupervised contrastive embedding training to optimize text representation. Based on the prompt tuning method and the iterative training over unlabeled data, the model further utilizes the knowledge from the pre-training stage. Experiment results on tweet data show that our CIPTA achieves encouraging performance in public opinion analysis.
Spirometer and sEMG respiratory patterns for clinical decision support system

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.
RacPixGAN: An Enhanced Sketch-to-Face Synthesis GAN Based on Residual modules, Multi-Head Self-Attention Mechanisms, and CLIP Loss

In this paper, we present an enhanced model to overcome the drawbacks of the traditional Pix2pix GAN (Image-to-Image Translation with Conditional Adversarial Networks) in generating performance for sketch-to-face synthesis. This model integrates residual modules and multi-head self-attention mechanisms. Additionally, to enhance the model’s generative capabilities in sketch-to-face synthesis tasks, we introduce a brand-new loss function called CLIP (Contrastive Language-Image Pretraining) Loss. We begin by providing a comprehensive overview of the key theories and techniques for our model. Then, we empirically test the upgraded model and contrast it with the traditional Pix2pix GAN. The experimental outcomes demonstrate that the new model significantly outperforms the traditional Pix2pix GAN in terms of generating performance for sketch-to-face synthesis tasks, supporting the idea that adding residual modules and multi-head self-attention mechanisms can significantly improve the generator’s performance in such tasks. The addition of CLIP Loss has also been shown to improve the quality of image generation.
A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads.
A Research on Attraction Details Question-answer Pair Generation based on Gate Attention Unit

The promotional information on the current scenic site is mixed and not clear enough. The long text information can be refined into multiple question-answer (QA) pairs by the question-answer pair generation (QAG). The vast majority of the standard QAG models depend on self-consideration unit, and there are cases that the created answers are not sufficiently precise and the produced questions and answers don’t coordinate. We propose an encoder-decoder architecture based on the Gate Attention Unit (GAU) to handle the QAG task. We found that the GAU applied directly to the encoder-decoder structure, that cross-attention based on GAU can’t handle the information from the encoder and decoder well with a single head attention unit, and that the problem can be effectively solved by using a multi-head attention mechanism. In this work, experiments are conducted to identify the appropriate model structure for the GauFormer with the best outcome on the QAG task for attraction detail. This demonstrates that GAU is capable of handling more text generation work.
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling

Federated learning allows multiple parties to jointly train deep learning models without the need for any participants to reveal their private data to a centralized server. However, this form of privacy-preserving collaborative learning has resulted in significant communication costs during the training period. To address this problem, we propose the Deep Hierarchical Quantization Compression (DHQC) algorithm. DHQC reduces the communication cost by using gradient sparsification and multi-bit quantization, and improves the accuracy and convergence speed of the model by using a dynamic sampling strategy. Experimental results on the CIFARIO and CIFARIOO datasets demonstrate that compared with the uncompressed baseline, DHQC can achieve a compression rate of 128x, significantly reducing the communication costs.
A New Strategy for Improving the Accuracy in Scene Text Recognition

Scene text recognition is the process of recognizing and converting text in natural images, such as street signs or billboards, into machine-readable text. However, there are some complexities, background variability, and even serious interference, and the text may also have diversity, which can lead to errors in recognition. This paper proposes an optimized text recognition strategy that can reduce errors by combining convolutional neural networks and recurrent neural networks to achieve accurate recognition of text in text images. A sequence learning model based on recurrent neural networks was used to process text sequences of different lengths, and CTC Loss was used to train the model. In addition, we also used data augmentation methods to increase the diversity of training data, in order to improve the robustness and generalization ability of the model. We conducted experiments on multiple public datasets to validate the performance and effectiveness of our proposed model. The experimental results showed that our method has reached the level of state-of the-art methods in terms of accuracy in detection results.
An Extensive Study of the Structure Features in Transformer-based Code Semantic Summarization

Transformers are now widely utilized in code intelligence tasks. To better fit highly structured source code, various structure information is passed into Transformer, such as positional encoding and abstract syntax tree (AST) based structures. However, it is still not clear how these structural features affect code intelligence tasks, such as code summarization. Addressing this problem is of vital importance for designing Transformer-based code models. Existing works are keen to introduce various structural information into Transformers while lacking persuasive analysis to reveal their contributions and interaction effects. In this paper, we conduct an empirical study of frequently-used code structure features for code representation, including two types of position encoding features and AST-based structure features. We propose a couple of probing tasks to detect how these structure features perform in Transformer and conduct comprehensive ablation studies to investigate how these structural features affect code semantic summarization tasks. To further validate the effectiveness of code structure features in code summarization tasks, we assess Transformer models equipped with these code structure features on a structural dependent summarization dataset. Our experimental results reveal several findings that may inspire future study: (1) there is a conflict between the influence of the absolute positional embeddings and relative positional embeddings in Transformer; (2) AST-based code structure features and relative position encoding features show a strong correlation and much contribution overlap for code semantic summarization tasks indeed exists between them; (3) Transformer models still have space for further improvement in explicitly understanding code structure information.
Graph Neural Network Model Based on Layered Converter Aggregation

Graph data is an important data in real society. With the development of neural network technology, graph neural networks are receiving more and more attention. However, the existing methods mostly use adjacency matrix or attention to aggregate the information of surrounding nodes, or use random walk to select neighboring nodes for aggregation, but this aggregation method is too simple and ignores the complexity of the graph structure. At the same time, inputting all the massive graph data into GCN for large-scale matrix operations in the real world requires a lot of computational power, and the attention calculation between multiple nodes also brings a heavy burden to computers, resulting in additional time loss. Based on this, this article proposes a hierarchical transformation aggregation model to aggregate node information by hierarchy and region. To put it simply, we first select nodes in each layer in the way of random walk, and then use GCN to aggregate. After obtaining the node characteristics of each layer, we use the attention mechanism with transformation to aggregate the node characteristics of each layer to obtain the final representation of nodes. Finally, we verify the reliability of the model on multiple datasets.
A Just-in-time Software Defect Detection Method Using Generative Adversarial Networks

As a branch of software defect detection research, Juts-in-time software defect prediction mainly detects defects with the detection granularity of change level. Change-level defect detection means that when the project developer commits code, whether there are defects in the introduced code are detected. When the defect detection model classifies the code submitted by project developers as introducing defects to commit changes, developers need to modify their submitted changes to be classified as clean classes. Investigation shows that some project developers choose to ignore the classification results of the software defect detection model, and only modify the code structure in the submitted changes, but not the code semantics, so that the software defect detection model marks their submitted changes as clean changes. This action by the developer is often referred to as a semantically preserved change. This paper introduces a just-in-time software defect detection model based on generative adversarial networks. The semantic equivalent code segments are generated by the generative adversarial model, and the generated data and original data are used to train the defect detection model, to improve the performance of the software defect detection model. The proposed method is tested on six project datasets commonly used in the field of just-in-time defect detection. The experimental results show that the proposed method can effectively detect whether there are software defects introduced by semantically preserved changes in the submitted changes.
Design and Development of Emotional Analysis System for Chinese Online Comment Text

Performing sentiment analysis on a massive volume of online comments data has significant commercial value. Therefore, this paper proposes a sentiment analysis platform focusing on Chinese comment text. In system design, we utilized deep learning techniques and implemented a large-scale pre-trained language model ERNIE for text feature extraction. We fine-tuned the network structure targeting downstream tasks to achieve Chinese sentiment analysis, overcoming the challenges of traditional machine learning methods, such as low accuracy and scalability. Additionally, we compared the experimental results with prominent pre-trained models, analyzed and evaluated the experimental data. The experimental results indicate that the platform can enhance the data analytic capabilities of online Chinese comment text, ameliorate the communication efficiency between industry audiences and creators, and extract commercial values from Internet comments.