Reviewerly/exHarmony huggingface dataset:
Improving Machine Reading Comprehension by Integrating External Knowledge through Attention Mechanism and Graph Neural Network

Extractive machine reading comprehension task as a branch of reading comprehension tasks that requires extracting appropriate text fragments from passages as answers to relevant questions. In this paper, we use ConceptNet as an external world knowledge source for the reading comprehension task, and generate knowledge graphs for each question and passage after retrieving knowledge from ConceptNet. The role of Graph neural network is to derive knowledge on a knowledge graph and integrate it into the context of the corresponding question and passage. For better integration with external knowledge and deeper understanding of semantic relationships between questions and passages, attention mechanisms are introduced to accomplish further integration.
A Named Entity Recognition Method Combining Keyword and Self attention Mechanism

As the basis of natural language processing tasks such as information extraction and machine question answering, named entity recognition has important research significance. However, traditional models have less consideration for utilizing the feature information of the text. To address this issue, this paper proposes a named entity recognition model that integrates keyword and self attention mechanism based on the HBT model.The model has made two improvements from the perspective of enhancing the utilization of text's own feature information: one is to introduce keyword feature vectors, and the other is to use multi head self attention mechanism for encoding layer vectors. The experimental results show that the improved model achieves an F1 value of 70.74% on the DuIE2.0 dataset, which is significantly improved compared to the benchmark model.
Multi-Branch ResNet-Transformer for Short-Term Spatio-Temporal Solar Irradiance Forecasting

The increasing penetration of solar generation into power grids has promoted the need for accurate and reliable short-term solar irradiance forecasting. Existing methods utilizing advanced deep learning architectures have shown advanced performance compared to conventional time-series analytical techniques but in general encountered shortcomings in modeling spatial correlations among neighboring solar generation sites, exploring the similarity of long-term, time-varying patterns, and alleviating overfitting issues in convolutional and recurrent neural networks, such as the popular Long Short-term Memory (LSTM). To effectively but yet reliably tackle these challenges in the existing literature, this article proposes a spatio-temporal framework consisting of a multi-branch hybrid Residual network and the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Transformer</monospace> architecture (ResTrans). The proposed framework has been tested on two groups' real-world data containing 17 years-long data from different solar sites in Philadelphia, USA, including 12 and 18 locations, respectively. Compared to other hybrid benchmark architectures, including single-branch ResTrans and multi-branch ResNet-LSTM (ResLSTM), single-branch ResLSTM, and CNN-LSTM, the proposed multi-branch ResTrans achieves the highest forecasting accuracy with an average RMSE of 0.049 (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), an average MAE of 0.031 (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> coefficient of 97%.
SQ-CARS: A Scalable Quantum Control and Readout System

Qubits are the basic building blocks of a quantum processor which require electromagnetic pulses in giga hertz frequency range and latency in nanoseconds for control and readout. In this paper, we address three main challenges associated with room temperature electronics used for controlling and measuring superconducting qubits: scalability, direct microwave synthesis, and a unified user interface. To tackle these challenges, we have developed SQ-CARS, a system based on the ZCU111 evaluation kit. SQ-CARS is designed to be scalable, configurable, and phase synchronized, providing multi-qubit control and readout capabilities. The system offers an interactive Python framework, making it user-friendly. Scalability to a larger number of qubits is achieved by deterministic synchronization of multiple channels. The system supports direct synthesis of arbitrary vector microwave pulses using the second-Nyquist zone technique, from 4 to 9 GHz. It also features on-board data processing like tunable low pass filters and configurable rotation blocks, enabling lock-in detection and low-latency active feedback for quantum experiments. All control and readout features are accessible through an on-board Python framework. To validate the performance of SQ-CARS, we conducted various time-domain measurements to characterize a superconducting transmon qubit. Our results were compared against traditional setups commonly used in similar experiments. With deterministic synchronisation of control and readout channels, and an open-source approach for programming, SQ-CARS paves the way for advanced experiments with superconducting qubits.
A Code Completion Approach Based on Abstract Syntax Tree Splitting and Tree-LSTM

Code completion refers to automatically generating the missing parts of code based on existing code snippets. Code completion can help programmers improve their coding efficiency and reduce errors. Providing accurate suggestions at the desired following token location in the completion recommendation list can significantly assist users. We have advanced the research on the next token prediction in code completion. Our work utilizes GPT-2 as the underlying architecture, and research has shown the effectiveness of incorporating the structural information of abstract syntax trees (ASTs) in code prediction. Particularly, predicting the type information of the next token has shown significant improvements. Our work proposes an algorithm to segment abstract syntax trees while preserving their structural characteristics. We use Tree-LSTM to extract the structural information of ASTs. We conducted experiments on a standard dataset and compared the effects of removing different components from the approach to validate its effectiveness.
Face Deformation Under Feature Transfer and Geometric Deformation

Image deformation refers to deforming objects in images into a target shape or posture. Although point-based image deformation algorithms have made breakthroughs in performance and visual effects, they are limited to warping the current structure information in the source image. For example, while opening a closed mouth in a face deformation, the point-based algorithm cannot generate teeth, which causes the mouth to twist weirdly. Deep learning-based face editing models can generate new parts but cannot achieve fine pixel-level manipulation. To overcome these challenges, we propose a two-step strategy for face deformation. We first generate a high-resolution intermediate image by blending the source image and the specific part of the target image via our face blending generative adversarial network (FB-GAN). Then, we employ a state-of-the-art point-based geometric deformation method to deform the intermediate image with target face guidance. Extensive experiments show that the proposed FB-GAN can generate realistic and high-resolution results and demonstrates that the two-step face deformation strategy can be better applied to human face deformation.
A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.
Institutional innovation essence and knowledge innovation goal of intellectual property law in the big data era

This work aims to expand the understanding on the nature of institutional innovation and the goal of knowledge innovation of intellectual property (IP) law in the era of big data (BD) and to avoid the risks of information security in the IP service system. On the basis of the era of BD, the nature of institutional innovation and the goal of knowledge innovation in IP law are systematically discussed. Combined with smart contract (SC) technology, an IP service system based on blockchain technology is designed to ensure the security of IP services. The SC is optimized from the perspective of concurrency, and the contract transactions (CTs) are clustered. Moreover, considering the execution time (ET) and available resources of CTs, a speculative concurrency control algorithm that can realize intelligent contract scheduling is proposed. On the basis of the ciphertext-policy attribute-based encryption (CP-ABE) algorithm, a policy-updatable attribute encryption algorithm is designed. The improved CP-ABE algorithm includes six steps: system initialization, attribute private key generation, encryption, decryption, ciphertext policy generation, and ciphertext policy update. The identity parameter of the traceability manager is introduced into the improved CP-ABE scheme, which significantly increases the amount of computation of bilinear mapping, thus resulting in the time cost of encryption and decryption being approximately 350 ms and 300 ms higher than that of the CP-ABE scheme. Using the technical mode of "blockchain + SC," the design of the IP service system is realized, which has important reference value for the institutional innovation of IP law.
Fault Prediction in Electric Power Communication Network Based on Improved DenseNet

Power communication network plays a crucial role in the power system infrastructure. However, its rapid development has led to a significant increase in real-time alarm volume, posing great challenges to network management personnel. In the event of a fault, the impact on normal work and daily life of users can be substantial. Thus, fault prediction for the power communication network is essential for effective equipment management and maintenance, as well as for improving system reliability. In this paper, we propose a multichannel fault prediction model for power communication network based on an improved DenseNet and Transformer. This method encodes the features into multiple channels, and proposes the Improved Channel Attention Module(ICAM) to the DenseNet architecture to suppress non-significant features. Transformer is then utilized for prediction, thereby improving the accuracy of fault prediction. Simulation experiments conducted on the Telstra dataset demonstrate that the algorithm proposed in this paper outperforms the compared algorithms, and can effectively predict faults in the power communication network, providing early warnings for network operators.
Knapsack Encryption with Elliptic Curve Cryptography Based Secured Wireless Network

This research study discusses about the prospective future research areas while presenting the state-of-the-art in wireless network cryptography. Elliptic Curve Cryptography (ECC) offers a safe way for interacting nodes to exchange keys. In order to implement ECC, the message is first converted into an affine point on the Elliptic Curve (EC), and then the knapsack method is applied to the ECC-encrypted message across the finite field. This approach presents Knapsack Encryption with ECC Algorithm (KECC) for improving data security in wireless networks. The data is encrypted by using the knapsack algorithm and the ECC algorithm. The necessary encrypted data is forwarded to the receiver via in-between nodes. As a result, it avoids the man-in-the-middle attack. Experimental results prove that the KECC strategy increases the forward ratio. In addition, it reduces the key generation, encryption, as well as decryption computational time.
Logical-Cluster-Based Personalized Federated Multi-Task Learning for Internet of Vehicles

Federated learning (FL) is an emerging distributed machine learning paradigm that emphasizes user privacy. The majority of current federated learning systems are oriented for single task while participants in the Internet of Vehicles (IoV) demand to train different models for multiple intelligent services simultaneously. As one of transfer learning methods, multi-task learning (MTL) has the potential to integrate with federated learning to realize personalized local training. However, the existing federated multi-task learning (FMTL) algorithms are faced with the problems of high implementation complexity and communication overhead. To solve the above issues, we propose a logical-cluster-based personalized federated multi-task learning framework named pFMTL. In the framework, the multi-task model is decomposed into a basic module for extracting features and K task-specific modules for outputting inferences. We leverage logical clusters and multi-task learning to enhance the personalization and generalization capability of task models, respectively. To improve the communication efficiency further, we also design a module-wise task scheduling strategy, which supports both user module scheduling and cluster aggregation scheduling to ensure the convergence of multi-task model with less communication overhead. Finally, the simulation results imply that pFMTL can increase task accuracy and reduce communication latency compared with other benchmarks.
DiffLIE: Low-Light Image Enhancment based on Deep Diffusion Model

Low-light Image Enhancement (LIE) has received significant attention in the field of computer vision low-level tasks in recent years. Previous work mainly treated LIE as a lighting enhancement work based on the Retinex theory. In this paper, we propose a new approach that utilizes the powerful generative network, the deep diffusion model, to regard LIE as a task of generating normal illumination images from pure black images. We modeled the enhancement process of low-light images based on the Markov assumption of conditional probability and constructed a controllable illumination enhancement model with diversified outputs. The experimental results achieved excellent visual effects and PSNR indicators on various synthetic datasets and real datasets.
A Study on Sentiment Analysis of Movie Reviews based on ALBERT-TextCNN-HAN

With the proposal of Albert model and the fusion application of Textcnn model, the ability of emotion analysis has been greatly improved. However, the Albert-TextCNN model is limited in its capacity to identify key words and sentences in the text, impeding the formation of a comprehensive text representation through these important elements. To address the shortcomings of the Albert-TextCNN model, we have proposed adding an attention mechanism to the model. However, since single-word-level attention fails to fully consider the significance of text hierarchy and inter-sentence relationships, the Albert-TextCNN model is combined with the Han model (Hierarchical Attention Network) to enable the model to allocate different levels of attention to sentences and words of varying importance in the text. This two-level attention mechanism better extracts key information in comments. Compared to the original model, the Albert-TextCNN-Han model more accurately distinguishes between different emotions and improves the performance of emotional analysis in film reviews. This improved accuracy can help individuals select movies that align with their preferences, and manufacturers can precisely obtain customer feedback.
Deep Learning based Opinion Mining on Throat Cancer Social Media Posts

Twitter has become a popular platform for people to share their thoughts and opinions with the world. It allows users to post openly on any topic, giving them the freedom to express themselves without fear of judgment or censorship including those relevant to throat cancer. Twitter sentiment analysis is an important tool for understanding the relative sentiment of the public for certain topics or ideas present on the platform. By using Natural Language Processing (NLP) techniques on millions of tweets, Sentiment Analysis determines how likely each tweet falls into a pre-defined positive or negative classification. The tweets will be classified into three categories using the Lexicon, CNN, LSTM, and CNN-LSTM: positive, neutral, and negative. This study examined the use of text tweets from Twitter as a source of data. Curated tweets from public accounts were utilized and a total of 30002 tweets were collected. The study suggests that the use of Lexicon, CNN, LSTM, and CNN-LSTM approaches can enhance accuracy when conducting a classification task. Through this process, 82% accuracy has been obtained with 24000 positive tweets and 6000 negative tweets.
System Design and Research of Future Broadcasting Service Based on Blockchain

With the rapid development of information technology, broadcasting services provide a lot of convenience for people's life. The way people obtain information services has become more and more abundant, the penetration rate of broadcasting services has increased and the number of multimedia applications has increased day by day, and the scale of the number of network users has been significantly improved. However, there are still some obvious new pain points in today's multimedia broadcasting service system. This article will combine actual needs, use blockchain technology, and use smart contracts as the support to design a future broadcast service system. Relying on emerging technologies, the system deeply optimizes the relationship between traditional content distribution and users, and can be more suitable for a new generation of future broadcast services.
Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure.
A New Dynamic Threshold Based Energy Saver Resource Allocation method for Cloud Infrastructure

High demand for cloud computing resources has given rise to the enormous size of cloud data centers. Consequently, the energy demand for cloud resources has increased. This is high time to put a check on energy consumption to make cloud computing more profitable for the cloud service provider and to defend the environment from carbon footprint. In this paper, a method has been proposed to allocate the resources to the coming tasks in an energy efficient manner. The proposed method of host selection for VM consolidation is successful in the reduction of energy consumption and maintaining the SLA violations low.
Multimodal Framework for Multilingual Social Interaction Assistive Application (MSIAA) for Elderly People

Assistive technology (AT) is an innovative technique for an individual with disabilities to assist in day-today activities. Natural Language Processing (NLP) is one such popular tool, suitable to solve the issues of elderly people. Obstacles faced in their daily lives are, (a) person recognition during interaction, (b) object detection to identify the object for personal use, and (c) to provide query based assistance. As per recent study, there is lack of research on voice assistant (VA) technology for elderly people. The VA, home assistant has a great impact on assisting an individual based on human-computer interaction. It enables quick access to the required services to perform the task. A brief review on AT is discussed in TABLE I. The purpose of this work, is to propose a multimodal framework to address elderly people challenges. The proposed model achieves overall, 68% of accuracy.
A Natural Language Processing for Sentiment Analysis from Text using Deep Learning Algorithm

Sentiment analysis has its large application in a natural language processing. Natural language processing have a large range of applications like machine translation, aspect-oriented product analysis, product reviews, text classification and sentiment analysis for spam filtering and email categorization. Lexicons are widely used in emotion detection systems can be defined as a list of words that the emotions convey or complex machine learning algorithms. In this implementation, BERT (Bidirectional Encoder Representations for Transformers) is used as a deep learning-based unsupervised method for natural language processing that enables computers to understand text representations in terms of context when performing tasks like question-answering, language inference, and text summarization. The suggested method divides the text into various emotional states, such as neutral, sadness, fear, joy, anger, etc.
Enhanced Deep Convolutional Neural Networks for Facial Mask Recognition using MobileNetV2 Transfer Learning Techniques

COVID-19., the corona virus which is an infectious disease that affect the day-to-day life of every individual person in the world. Masks must now be worn in public spaces to reduce the spread of the disease. So., detecting face mask is very important in public places., crowded areas like shopping malls., workplaces., public transport. The existing models may not be able to predict the output for the images with reflective background., require heavy computational load and provide less accuracy. Face mask identification is accomplished in using machine learning techniques such as Tensorflow., Keras., and OpenCV. This proposed model recognizes faces in images or videos and makes predictions about whether a person or group of individuals is wearing a mask correctly., wrongly., or not at all. It also distinguishes between faces with just masks and faces that are covered by additional things. Convolutional Neural Networks (CNN) are used in the proposed model to determine the output and train the dataset. The system's accuracy was 95%.