Reviewerly/exHarmony huggingface dataset:
An efficient multiple-word embedding-based cross-domain feature extraction and aspect sentiment classification

As the size of the feature space and data size increases, it is difficult to find the essential key features for cross-domain classification problems. Traditional word embedding and feature selection models use limited-sized data and dimensions for feature ranking and classification processes. To overcome these challenges, a hybrid cross-domain classification (CDC) based feature selection has been proposed to improve the efficiency of aspect sentiment classification on large databases. Initially, the crawled data from the Amazon product dataset to the cloud web server. In the data filtering approach, each record is pre-processed for the noise removal process. In the CNN framework, the different word-embedded models are used to ensemble the features from the training data. Here, TF-ID+Word2Vec, Hybrid Glove, and Skipgram are used to select the relevant key features from the training data. On the local side, remote IoT-based devices data distributed processing. For in-depth analysis of the sentiments IOT sensors have been used for automation. The filtered key features are given to the proposed feature extraction measure to select the essential features for the cross-domain classification model, it classifies the positive or negative. The proposed CDC model achieves overall precision and recall is 0.96 and 0.97 respectively. Experimental results proved that the proposed cross-domain feature selection-based classification approach has a better overall true positive than the conventional approaches.
PCBIR-CV: A privacy-preserved content-based image retrieval using combined visual descriptors for cloud

Privacy-preserved retrieval of images based on the contents is a challenging task in encrypted domain. We introduce a prototype that implements a privacy-preserved content-based image retrieval for cloud using combined descriptors. The prototype ensures high retrieval performance compared with state-of-the-art schemes using standard image dataset. The prototype provides an experimental model that can be utilized for developing and validating newer schemes in the area. Moreover, this prototype can be migrated to real-world applications that mandate secure retrieval of encrypted images outsourced on cloud servers.
MEinVR: Multimodal interaction techniques in immersive exploration

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manip- ulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.
Adaptive Feature Fusion and Kernel-Based Regression Modeling to Improve Blind Image Quality Assessment

In the fields of image processing and computer vision, evaluating blind image quality (BIQA) is still a difficult task. In this paper, a unique BIQA framework is presented that integrates feature extraction, feature selection, and regression using a support vector machine (SVM). Various image characteristics are included in the framework, such as wavelet transform, prewitt and gaussian, log and gaussian, and prewitt, sobel, and gaussian. An SVM regression model is trained using these features to predict the quality ratings of photographs. The proposed model uses the Information Gain attribute approach for feature selection to improve the performance of the regression model and decrease the size of the feature space. Three commonly used benchmark datasets, TID2013, CSIQ, and LIVE, are utilized to assess the performance of the proposed methodology. The study examines how various feature types and feature selection strategies affect the functionality of the framework through thorough experiments. The experimental findings demonstrate that our suggested framework reaches the highest levels of accuracy and robustness. This suggests that it has a lot of potential to improve the accuracy and dependability of BIQA approaches. Additionally, its use is broadened to include image transmission, compression, and restoration. Overall, the results demonstrate our framework’s promise and ability to advance studies into image quality assessment.
A fuzzing scheme for microkernel operating system based on relationship between system calls

Compared with the macrokernel, the microkernel operating system has higher kernel stability and security because the microkernel only includes the key parts of the operating system such as memory management. But once there is a bug with the microkernel, it will affect the entire operating system. In order to improve the security and stability of the microkernel operating system, this paper proposes a fuzzing scheme for the microkernel based on the relationship between system calls. The scheme deeply analyzes the correlation between system calls in the microkernel based on static analysis. Then the generation process of subsequent test cases is guided by the correlation. Finally, this paper conducts experimental analysis on the scheme to verify the correctness and effectiveness of the scheme.
Research on unlabeled target domain adaptive information processing based on TrAdaBoost algorithm

In recent years, information processing technology based on transfer learning has developed rapidly, and more and more data users do have not enough time to complete data labeling. Domain Adaptation (DA) information processing for unlabeled data is becoming increasingly important. To improve the performance of the model in the target domain information processing, we design a target domain information network and propose a collaborative learning model between the private network and the regional adaptive network based on the TrAdaBoost algorithm. Different from the traditional method, this model can avoid directly reducing the difference between domains as the model optimization goal, and the target is optimized for cluster regularization, driving the target domain data points closer to the cluster center. And further, promote the training of domain information networks. Through simulation experiments, it is concluded that under this model, the target domain private network is effectively trained based on the TrAdaBoost algorithm under the specified target domain, to achieve better information processing performance in the target domain.
A web service recommendation method based on reputation evaluation in small world heterogeneous network

In order to improve the performance of web service recommendation in small world heterogeneous network, this paper proposes a method based on reputation evaluation. The method defines the calculation method of reputation between users and between users and services, and then proposes a clustering algorithm based on WOA to cluster users and services, which greatly simplifies the calculation of recommendation. Experimental results show that the proposed method is effective in web service recommendation.
Quantum hash function of continuous-time quantum walk based on cycle under broken-line decoherent noise

The existing hash functions based on quantum walk construction are all proposed under the ideal closed environment. In order to better conform to the actual situation and improve the relevant performance of Hash functions, we propose a quantum hash function constructed by continuous quantum walk on the basis of broken-line noise model. We first propose a continuous-time quantum walk model on cycle under the influence of broken-line decoherence, after that apply this model to the construction process of the quantum hash function and put forward a building method of the hash function on the basis of the broken-line decoherent continuous-time quantum walk on the one-dimensional cycle. The safety analysis and numerical simulation indicate that our hash function has greater anti-collision performance than the existing hash function based on the quantum walk.
Research on algorithm of students' classroom behavior recognition based on two-stream convolutional neural network

With the rise of new technologies, people hope to have a better experience and feeling in their daily life, and the technology and application of various industries are developing towards the direction of digitalization, networking and intelligence. The digital reform of colleges and universities has become an important means of the informatization development of colleges and universities. Its significance lies in improving the teaching quality, strengthening the teaching management norms, optimizing the construction of teaching resources, etc. In this paper, a class behavior recognition algorithm based on two-stream convolutional neural network is studied. The purpose of the research is to analyze and evaluate the teaching quality of the classroom through the identification and statistics of students' behaviors, so as to improve the teaching methods according to the feedback results, and finally achieve the purpose of improving the teaching effect.
Cross-Modal Sentiment Analysis of Text and Video Based on Bi-GRU Cyclic Network and Correlation Enhancement

Cross-modal sentiment analysis is an emerging research area in natural language processing. The core task of cross-modal fusion lies in cross-modal relationship extraction and joint feature learning. The existing research methods of cross-modal sentiment analysis focus on static text, video, audio, and other modality data but ignore the fact that different modality data are often unaligned in practical applications. There is a long-term time dependence among unaligned data sequences, and it is difficult to explore the interaction between different modalities. The paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology in unaligned data scenarios, which can perform sentiment analysis on unaligned text and video modality data in social media. Firstly, the model adds a cyclic memory enhancement network across time steps. Then, the obtained cross-modal fusion features with interaction are applied to the unimodal feature extraction process of the next time step in the Bi-directional Gated Recurrent Unit (Bi-GRU) so that the progressively enhanced unimodal features and cross-modal fusion features continuously complement each other. Secondly, the extracted unimodal text and video features taken jointly from the enhanced cross-modal fusion features are subjected to canonical correlation analysis (CCA) and input into the fully connected layer and Softmax function for sentiment analysis. Through experiments executed on unaligned public datasets MOSI and MOSEI, the UA-BFET model has achieved or even exceeded the sentiment analysis effect of text, video, and audio modality fusion and has outstanding advantages in solving cross-modal sentiment analysis in unaligned data scenarios.
Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price

The low-energy task scheduling of cloud computing systems is a key issue in the field of cloud computing. Nevertheless, existing works on task scheduling lack consideration of the conflict relationship between tasks and focus on heuristic and other approximate algorithms. Thus, solving the problem of minimizing energy consumption with antiaffinity constraints between tasks and designing an efficient exact algorithm for task scheduling is a major challenge. This paper abstracts the problem into a multidimensional bin packing model with conflict constraints. The model is decomposed by the Lagrange relaxation principle and Dantzig–Wolfe decomposition principle. Moreover, we propose an accurate algorithm based on branch-and-price. The algorithm benefits from a new initial solution generation scheme based on maximum cliques and dominant resource proportion, and a multipattern branching strategy. The efficiency of the proposed branch-and-price algorithm is verified by a number of numerical experiments.
Data-Driven Minute-Ahead Forecast of PV Generation with Adjacent PV Sector Information

This paper proposes and validates a data-driven minute-ahead forecast model for photovoltaic (PV) generation, which is essential for real-time micro-grid scheduling. Unlike day-ahead PV forecasts that heavily rely on weather forecast information, our proposed model does not require such data as it operates in an ultra-short-term time domain. Instead, the model leverages the generation data of the target PV sector and its adjacent sectors to capture short-term factors that affect electricity generation, such as the movement of clouds. The proposed model employs a long short-term memory (LSTM) network to process the data. By conducting experiments with real PV site data, we demonstrate that the information from adjacent PV sectors improves the accuracy of minute-ahead PV generation forecasts by 3.66% in the mean squared error index and 1.19% in the mean absolute error index compared to the model without adjacent sector information.
Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies

The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.
Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.
From reactive to proactive load balancing for task‐based parallel applications in distributed memory machines

Summary Load balancing is often a challenge in task‐parallel applications. The balancing problems are divided into static and dynamic. “Static” means that we have some prior knowledge about load information and perform balancing before execution, while “dynamic” must rely on partial information of the execution status to balance the load at runtime. Conventionally, work stealing is a practical approach used in almost all shared memory systems. In distributed memory systems, the communication overhead can make stealing tasks too late. To improve, people have proposed a reactive approach to relax communication in balancing load. The approach leaves one dedicated thread per process to monitor the queue status and offload tasks reactively from a slow to a fast process. However, reactive decisions might be mistaken in high imbalance cases. First, this article proposes a performance model to analyze reactive balancing behaviors and understand the bound leading to incorrect decisions. Second, we introduce a proactive approach to improve further balancing tasks at runtime. The approach exploits task‐based programming models with a dedicated thread as well, namely . Nevertheless, the main idea is to force not only to monitor load; it will characterize tasks and train load prediction models by online learning. “Proactive” indicates offloading tasks before each execution phase proactively with an appropriate number of tasks at once to a potential victim (denoted by an underloaded/fast process). The experimental results confirm speedup improvements from to in important use cases compared to the previous solutions. Furthermore, this approach can support co‐scheduling tasks across multiple applications.
Multimodal knowledge graph representation learning with entity description

The representation learning of knowledge graph aims to represent the semantic information of entities and relations as dense low-dimensional real-valued vectors and map them to the same low-dimensional space. Existing methods often focus on the single-modal information of the text and ignore the information of the image modality, resulting in the ineffective use of entity feature information in the image. And there is entity-related descriptive information in most knowledge graphs, which are not well used in current multimodal knowledge representation learning methods. In this regard, a multimodal knowledge representation learning method combining description information is proposed. This method combines multimodal (image, text) data to construct a knowledge representation learning model and combines its corresponding brief description information to improve the representation effect of multimodal data. Experimental results show that the method performs well on triple classification and link prediction tasks on the constructed WI-D dataset.
Cloud Security for Healthcare Services

The primary goal of this paper is to give the audience a set of principles to follow when purchasing cloud services to deliver healthcare services in order to ensure cybersecurity and the security of personal data processing, as well as a clear awareness of the accompanying obligations. The objectives are to provide a landscape of the applicable EU legislative instruments relevant to cloud services in the healthcare sector as well as an overview of the major cybersecurity and data protection challenges related to the security of personal data processing for cloud customers in the healthcare sector.
A Comparative Study on customer satisfaction between Amazon and Flipkart

The best management programmers recognize the importance of combining theoretical knowledge with real-world application. Training is an integral part of the curriculum because it provides students with the hands-on experience and broad perspective they need to succeed in their chosen fields. Indicative of the state of the economy and the country as a whole, it serves as a useful gauge. It's essential for success in the business world. The average graduate student has more self-assurance, intelligence, and worldview than the average person. Marketing research is essential to the success of modern businesses. In my opinion, this methodical and illuminating approach to market preparation is the way to go. This method has applications beyond simple commerce. I've decided to zero in on this topic because I believe it's crucial to acquire as much information as possible about any given topic, be it an idea, an event, an organization, a location, or a person. The first step is to fully immerse oneself in the relevant market in order to study its dynamics and locate openings to meet the felt or felt requirements.
Effective Quantization Evaluation Method of Functional Movement Screening with Improved Gaussian Mixture Model

Background: Functional movement screening (FMS) allows for the rapid assessment of an individual’s physical activity level and the timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on an improved Gaussian mixture model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors; then, we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately; finally, we conducted FMS assessment by using a maximum likelihood estimation. Results: The improved GMM has a higher scoring accuracy (improved GMM: 0.8) compared to other models (traditional GMM = 0.38, AdaBoost.M1 = 0.7, Naïve Bayes = 0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa = 0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task, and it is potentially feasible to use depth cameras for FMS assessment.