Reviewerly/exHarmony huggingface dataset:
Transforming Data Center Power Infrastructure: Embracing dynamic grid support and fuel cell solutions

Meeting ever-increasing capacity demands has emerged as a perceptual challenge for the data center industry. The continuous digitalization of businesses and society necessitates further capacity expansion, despite substantial investments in new infrastructure over the past five years. According to the Dell’Oro Group, investments in hyperscale data centers are projected to double in the next five years.
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation Scoring

Automatic pronunciation assessment models are regularly used in language learning applications. Common methodologies for pronunciation assessment use feature-based approaches, such as the Goodness-of-Pronunciation (GOP) approach, or deep learning speech recognition models to perform speech assessment. With the rise of transformers, pre-trained self-supervised learning (SSL) models have been utilized to extract contextual speech representations, showing improvements in various downstream tasks. In this study, we propose the end-to-end regressor (E2E-R) model for pronunciation scoring. E2E-R is trained using a two-step training process. In the first step, the pre-trained SSL model is fine-tuned on a phoneme recognition task to obtain better representations for pronounced phonemes. In the second step, transfer learning is used to obtain a pronunciation scoring model that uses a Siamese neural network to compare the pronounced phoneme representations to embeddings of the canonical phonemes and produce the final pronunciation scores. E2E-R achieves a Pearson correlation coefficient (PCC) of 0.68, which is similar to the state-of-the-art GOPT-PAII model while eliminating the need for training on additional native speech data, feature engineering, or external forced alignment modules. To our knowledge, this work presents the first utilization of a pre-trained SSL model for end-to-end phoneme-level pronunciation scoring on raw speech waveforms.
PySyComp: A Symbolic Python Library for the Undergraduate Quantum Chemistry Course

We report an educational tool for the upper level undergraduate quantum chemistry or quantum physics course that uses a symbolic approach via the PySyComp Python library. The tool covers both time-independent and time-dependent quantum chemistry, with the latter rarely considered in the foundations course due to topic complexity. We use quantized Hamiltonian dynamics (QHD) that provides a simple extension of classical dynamics and captures key quantum effects. The PySyComp library can compute various concepts regarding the fundamental postulates of quantum mechanics, including normalized wave functions, expectation values, and commutators, which are at the core of solving the Heisenberg equations of motion. It provides a tool for students to experiment with simple models and explore the key quantum concepts, such as zero-point energy, tunneling, and decoherence.
ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks

The reliability and accuracy of collaborative spectrum sensing in Cognitive Radio Networks (CRNs) is highly dependent on integrity of shared spectrum sensing data. This poses challenges if the protocols need to be realized on Internet of Things-based CRNs (IoT-CRNs) without additional resources. In this letter, we introduce ProMETHEUS, a secure and lightweight spectrum allocation protocol designed specifically for IoT-CRNs. ProMETHEUS utilizes a blockchain framework to ensure the integrity of shared spectrum sensing data and employs a simple pattern-based channel state prediction technique for efficient spectrum allocation. We implemented ProMETHEUS in an IoT-CRN using Raspberry Pi 3 devices connected with software-defined radios. Our evaluation demonstrates that ProMETHEUS outperforms recently proposed CRN protocols, such as ProBLeSS and PROLEMus.
Receiver Design for ICI-CSK System: A New Perspective Based on GRU Neural Network

In conventional chaotic communication schemes, the receivers mostly use energy detection methods. However, due to the limitations of this detection method, the initial condition index chaos shift keying (CSK) scheme cannot work over the multipath Rayleigh fading channel. To overcome this issue, a gated recurrent unit (GRU)-aided initial condition index CSK system, referred to as GRU-ICI-CSK system, is proposed in this letter. The proposed GRU-ICI-CSK scheme exploits the powerful learning and classification abilities of deep learning, which can achieve a high reliable bit-error-rate (BER) performance. Moreover, we analyse the complexity of the proposed scheme. Simulation results demonstrate that the proposed scheme can achieve superior BER performance compared to the conventional energy detection chaotic communication schemes over multipath Rayleigh fading channels. Besides, the network based on GRU of the proposed scheme has lower computational complexity and better BER performance compared to the state-of-the-art network based on long short-term memory. Thanks to these benefits, the proposed GRU-ICI-CSK scheme can be considered as an effective solution for low-power and short-range wireless communication.
A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.
GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis.
Research on Mask Wearing Intelligent Detection System based on YOLOv5

In order to remind people in some scenarios, which need mask protection, we need to realize high precision and efficiency mask wearing detection. In this paper, a mask wearing intelligent detection system was created based on YOLOv5. Specifically, a YOLOv5 model with 8 convolution layers was utilized, and the data set was trained and fine-tuned by collecting and labeling data of masks and none masks. The data set includes more than 5000 images of mask wearing state in real scenes, the majority of which were taken by the author and then reprocessed to increase the model’s capacity of generalization. In order to train and evaluate the model, the data set was divided into training set, verification set and test set, respectively. The results indicated that the system’s accuracy rate and recall rate achieved 0.924, 0.950, respectively. Also, in the test set, the detection speed was between 0.007 and 0.008 seconds. The system can detect mask wearing efficiently and accurately, which has good applicability prospects.
Application Research of Pointer Instrument Panel Extraction Algorithm Based on Deep Learning

Pointer-type meters can suffer from inefficiencies in using manual data reading due to the lack of digital interfaces. Traditional pointer gauge reading recognition algorithms can only operate in specific environments or fixed locations without high reliability. In this paper, by introducing the traditional dial extraction algorithm Hoff circle detection, we propose the improved Mask R-CNN deep learning algorithm for automatic recognition of pointer-type dashboard based on Mask R-CNN algorithm, and introduce the maximum pooling algorithm for image feature extraction and the improved method of using PrROIPooling pooling technique. Experiments show that the improved Mask R-CNN algorithm improves the target detection accuracy by 2.1% and the instance segmentation accuracy by 1.9%. Compared with the traditional dial localization algorithm, this algorithm has the features of hih accuracy and robustness.
Improving BERTScore via Word Similarity Matrix Iterative Alignment Method

BERTScore is based on a greedy matching approach that calculates sentence similarity by aligning word pairs with the maximum cosine distance. However, in cases where the machine-translated text and the reference translation have different lengths, or when the machine-translated text is not fully translated, the word alignment results may not be entirely accurate. To enhance the performance of BERTScore by obtaining more precise word alignment results, this paper adopts the word similarity matrix iterative alignment method. Experiments are conducted on the MQM dataset, and the results demonstrate that the improved BERTScore algorithm significantly advances the correlation with the MQM dataset. This proves that the word similarity matrix iterative alignment method obtains superior word alignment results compared to the greedy matching approach, and can effectively improve the performance of the BERTScore algorithm.
Data Augmentation for Improving CNNs in Medical Image Classification

Medical image is essential for physicians to diagnose diseases. And convolutional neural networks (CNNs) have gained momentum for computer-aided diagnosis (CAD) medical image. However, there are still challenges in CNNs related to a lack of data and class-imbalanced datasets. Data augmentation is utilized to address the issues mentioned above. In this study, the Pseudo-color enhancement algorithm (OCEAN and TWILIGHT), common linear transform, CLAHE enhancement, and K-means clustering algorithm were applied to pneumonia X-ray images for data augmentation. Thus, five processed datasets were finally obtained. The results indicated that five data-augmentation methods effectively improved the performance of CNNs for the pneumonia X-ray images binary classification detection, in which the CLAHE enhancement and OCEAN enhancement methods had the accuracy and F1 Score with 97.42%, 97.19%, and 97.42%, 98.36%, respectively. DenseNet121 showed the best classification performance of four CNNs utilized in this study, as evidenced by the area under the receiver operating characteristic (ROC) curves (AUC). To verify the generalizability of the two enhancement methods, CLAHE and OCEAN enhancement methods were applied to the Magnetic Resonance Imaging (MRI) dataset of Alzheimer's disease, which had the accuracy and F1 Score with 98.15% , 98.15%, and 97.22%, 97.68%, respectively. Therefore, these simple and efficient data-augmentation methods effectively improve the performance of CNNs for diagnosing medical image, which can provide theoretical reference for physicians to diagnose the above two diseases, and advance the application of clinical medicine.
Double precision search based on ACO and ABC for multi-UAV multi-target path planning

UAV path planning is an important role in the UAV autonomy, and has become a hotspot. In actual combat scenarios, multiple UAVs are needed to strike multiple targets in succession and eventually return to base. But, multiple UAV path planning for the multiple targets is a challenge for the optimal path quickly. To solve the problem, a double precision method is designed in this paper. ant colony algorithm and an artificial bee colony algorithm are used to enhance the exploration capability in the low precision search phase and the exploitation capability in the high precision search phase, respectively. Simulation results show that the method can avoid fall into a local optimum, and effectively improve the efficiency of the path planning for the multi-UAV multi-mission objective problem.
Ground target detection method on moving base based on improved YoloV5

Due to the poor stability of aerial detection equipment, the small proportion of ground target pixels and the occlusion of high-rise objects, air-to-ground target detection has brought great challenges. In order to detect ground targets accurately, we propose a modified YOLOV5 model based on visual attention.The ConvMixer architecture based on the hybrid idea is added to the backbone network of YOLOv5 to obtain more context information. In addition, the GhostNetV2 module is used to improve the neck network of YoloV5 to reduce the number of model parameters and calculation. K-means++ clustering algorithm is used to select the initial anchor box which is as close as possible to the global optimal solution. Finally, Wise-IoU evaluation index is used to improve the original IoU measurement. The experimental results show that the AP value of the proposed method is 0. 797, which is 6. 7% higher than that of YOLOv5. On the basis that the number of parameters and amount of computation are less than the original model, the method effectively inhibits the ground complex background interference and improves the accuracy of air-to-ground target detection.
A study of classroom learning attention discrimination method based on head posture detection

To monitor and analyze students' classroom attention, an approach of discriminating attention of the students based on the recognition of head position is presented.. The facial feature points of classroom monitoring images are detected using convolutional neural network, the face is tracked using ( pose from orthography and scaling with iterations, POSIT) algorithm, the rotation angle of the face is calculated, and the student's attention is analyzed based on the tilt angle of his or her head. The proposed model has an accuracy of 88.7% for attention detection, which allows for effective analysis of students' attention and provide a basis for evaluating course teaching.
Deep Hashing Retrieval of Dynamic Corpora

Knowledge-intensive language applications, like open-domain question answering, call for NLP systems to retrieve related evidence in a document collection. A efficient and scalable retrieval system is an ideal component in these applications. Up-to-data research developed sequence-to-sequence framework on this task, eschewing any format of term-frequency or query-document similarity calculation. This line of work greatly improved the efficient of retrieval but with the sacrifice of scalablity. This work proposes a new framework based on the seq2seq architecture, which can solve scalability issues that exist in such methods. Experimental results on the public dataset Natural Question demonstrate the effectiveness of this approach.
Emotional Polar Predictions Based on Matching Prompts

In recent years, pre-trained language models have motivated the study in various tasks in natural language processing(NLP). In a new paradigm instead of fine-tuning, reformulating downstream tasks as cloze tasks by add textual or continuous prompts is a popular measure for solving NLP tasks. However, the effect of prompt learning tends to be only ensured by appropriate prompts and precise verbalizers. To address this issue, we propose a prompt matching method based on trigger tokens to solve emotional polar prediction tasks. The proposed model can extract the aspect words and key adjectives from input text as trigger tokens. Meanwhile, by introducing external knowledge we expand trigger tokens which are applicated in verbalizers and prompts matching. According to experiments, we show that providing instances which share the similar emotional trigger tokens improves model performance. And tests on the hotel reviews dataset show that our model have more high precision rate. In few-shot scenario, We also show that the fewer the samples, the more our model outperforms fine-tuning models. These results demonstrate that automatically providing instances with similar emotions is a new method to solve text classification tasks in few-shot scenario.
Two-Stage Decoding Network for End-to-End Task-Oriented Dialog Systems

Task-oriented dialog model guides dialogue generation by identifying user intentions and incorporating external knowledge. The existing models based on end-to-end memory networks have two problems. First, most models use Bi-GRU or Bi-LSTM to encode the dialogue context. Such a sequential structure encoder makes it easy to cause the loss of long dialogue information, so the model cannot accurately identify the user's intention. Second, the previous model uses the output of the encoder as the query vector to search for external knowledge, which results in the query vector fusing too much information unrelated to the search for external knowledge, so it is difficult for the model to incorporate external knowledge effectively. We propose a Two-Stage Decoding Network (TSDN) based on end-to-end memory networks to mitigate these problems. Concretely, our model consists of a Bert encoder, external knowledge, and a two-stage decoder, where one stage is for extracting entity information and another is for generating the response. The Bert encoder has excellent capture of important information about the dialog context. The two-stage decoding strategy can better search for external knowledge and generate high-quality responses. Experimental results on two benchmark datasets show that our approach achieves higher BLEU scores and Entity F1 scores compared to the baseline model.
Aspect-Based Sentiment Analysis Using BiLSTM-GateCNN Architecture

Conducting sentiment analysis on a large amount of comment text is an important means of extracting potential information from the text. However, existing aspect-based sentiment analysis tasks face difficulties in effectively capturing the associations between words and thus cannot accurately extract aspect words, they also do not fully utilize contextual semantic features, which overlooks the connection between aspect words and the context. This study proposes an aspect-based sentiment analysis model based on the BiLSTM-GateCNN network. First, we integrate the self-attention mechanism into the BiLSTM layer to obtain sentence feature representations and aspect term information. Then, we use the GateCNN network with multiple filters in the convolution layer to effectively extract feature information for multiple aspects in each receiving field. Finally, the gated unit simultaneously generates aspect features and sentiment features, making the text more discriminative in emotion classification. By testing the proposed model on Restaurant domain reviews of the SemEval2016 benchmark dataset, experimental results showed that our proposed sentiment analysis model has achieved significantly better accuracy than the baseline model.
Research on Fall Detection Algorithm Based on Lightweight Feature Fusion

A lightweight feature fusion 3D convolution neural network, 3DLF-Net, was designed to address the problems of too single convolution kernel, lack of diversity, high network structure complexity, and parameter redundancy in a video based deep learning fall detection model. The Fusion module of this network architecture adopts a multi-channel structure to increase the width of the convolution neural network, and uses different size volume sets to process input features, improving the feature extraction ability of the same layer of the network. At the same time, a novel deep separable convolution structure is designed to reduce the complexity of the model. The experimental results show that 3DLF-Net has a high recognition accuracy for fall detection and a small amount of network parameters.
ChatAN: Interval Adaptive Normalization based on ChatGPT Knowledge Augmentation

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method.
Database Design in the Infrastructure Information Management System

This paper presents the design idea of building the infrastructure information management system based on remote sensing data. With the characteristics of the systems massive remote sensing data and the comprehensive application problem of heterogeneous data, unify the design specifications of the database is the most important. Then several data tables are designed by category. Finally some key technologies such as data storage are discussed . This infrastructure information management software has solved the problem of smoothly and quickly call of the massive underlying and heterogeneous data. It has realized the functions of infrastructure plotting, process supervision, statistical analysis and two-dimensional and three-dimensional visualization. This system has been applied in the professional domain to improve the work efficiency.