Reviewerly/exHarmony huggingface dataset:
iWriter: An Offloading Method for Indirectly Writing Remote Data

Remote Direct Memory Access (RDMA) is expected to offer lower latency and higher throughput than standard kernel-based TCP/IP networking due to features such as kernel bypass and protocol offloading. For this reason, RDMA is often used as an excellent optimization approach to implement fundamental operations such as indirect writes, which have an effect on the performance of distributed storage systems. However, using the RPC method to accomplish indirect writes sacrifices CPU cycles. On the other hand, using a one-sided method constrained by the RDMA Verbs APIs requires at least two Round-Trip Times (RTTs).In this paper, we propose iWriter, a novel offloading method that enables clients to indirectly write data into a remote host’s memory without involving the remote CPU or extra RTTs. In order to reduce CPU overheads, iWriter instructs the RDMA-capable NIC (RNIC) to carry out indirect writes using native RDMA primitives. This way, the request, execution and reply are all automatically completed by the RNIC in only one RTT. Using commercial RoCEv2 NICs, we implement iWriter without making any changes to the RDMA drivers and compare it with existing methods. The evaluation results show that iWriter reduces latency by 19.1% to 47.1% compared to the RPC method and by 11.4% to 18.2% compared to the one-sided method.
TBP: Tree structure Burst-sequence Padding Defense Against Website Fingerprinting

Website Fingerprinting (WF) attacks have been claimed to seriously threat users’ online privacy. Such surveillance is hard to be avoided even for those browsing through Tor. Therefore, a series of WF defenses have been proposed to alleviate this threat, however, those methods usually require high computation or additional infrastructure, and with increased bandwidth overhead. In this paper, we propose a novel tree structured perspective to describe the browsing process of a website, i.e., the browser usually requests website resources (with order, nested and citation correlations, etc.) through multiple threads, hence, specific parallel and causal relationships among website resources can be reflected from the tree structured perspective. Based on that, we therefore design an efficient low cost defense called Tree structure Burst-sequence Padding (TBP). TBP adds dummy burst sequences to mimic fake website resources to traffic traces, leading to effective destruction of the original tree structure. Specifically, by using multiple random distributions to determine the insertion timetable of dummy burst sequences, TBP increases the randomness of intra-class traces and blurs the boundary of inter-class traces. Extensive experimental results show that TBP is able to reduce the attakcer’s true positive rate by 60% with 55% data overhead, saving 44% data overhead compared to the state-of-art lightweight defense FRONT. Compared with FRONT, TBP can reduce the true positive rate of the attacks, including DF, Tik-Tok, Var-CNN, by about 15%.
How to Adapt RDMA Congestion Control Algorithm Based on Local Conditions

The use of RDMA in datacenter networks is gradually increasing. However, due to the relatively simple flow control and congestion control mechanisms in RDMA networks, they lack the ability to adapt to various network loads and environments. Therefore, congestion can easily occur under high load, which severely affects network performance. Recently, a new congestion control algorithm called Shaped-Quota was proposed, and the main ideas of the algorithm include receiver-driven, traffic shaping, and bandwidth reservation. In this paper, we analyze the short message limit N and bandwidth reservation ratio R mentioned in Shaped-Quota, provide a definition formula, and conduct experiments, which can help the Shaped-Quota algorithm better apply to various network scales, topologies, and traffic models.
A Study of Healthcare Team Communication Networks using Visual Analytics

Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients' survival outcome. However, it is challenging to uncover and understand such factors in the complex Multiteam System (MTS) communication networks representing daily HCP cooperation. In this paper, we present a study on MTS communication networks constructed with real-world cancer patients' Electronic Health Record (EHR) access logs. We adopt a visual analytics workflow to extract associations between semantic characteristics of MTS communication networks and the patients' survival outcomes. The workflow consists of a neural network learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. We provide the insights found using this workflow with two case studies and an expert interview.
Discrete logarithm problem in matrix

In this paper the discrete logarithm problem in matrix in finite fields is formulated, possible ways of solution are given.
Optimizing Virtualization for Client-Based Workloads in Cloud Computing

Cloud computing has transformed the IT field by offering adaptable and versatile resources to cater to the increasing demands of businesses and organizations. Virtualization technologies are instrumental in facilitating the efficient deployment and management of resources within cloud environments. However, there are notable concerns regarding the security implications of virtualization in the cloud. This research paper thoroughly examines the security aspects of virtualization technologies in cloud computing, primarily focusing on identifying potential weaknesses, risks, and strategies to mitigate security threats. Additionally, the study investigates the security features and mechanisms provided by leading virtualization platforms and management tools. It scrutinizes access controls, isolation methods, network security, data protection, and integrity mechanisms offered by virtualization technologies to safeguard the cloud infrastructure and customer data. Furthermore, the paper addresses emerging security concerns associated with containerization technologies, encompassing vulnerabilities related to container escape, risks stemming from shared kernels, and issues concerning image integrity. It explores the effectiveness of container security measures, such asisolation, sandboxing, and access controls, in reducing these risks. Lastly, the paper summarizes the main findings and provides recommendations to enhance the security of virtualization technologies in cloud computing. It emphasizes the importance of continuous monitoring, regular security updates, robust access controls, and threat intelligence integration to mitigate security risks and uphold a secure cloud infrastructure.
Exploring Expertise in Early Childhood Teachers' Play Reflection: Focusing on Relational Ontology

Based on the perspective of relational ontology, this study attempted to explore the expertise that appeared in the play reflection of early childhood teachers. To this end, interviews with two early childhood teachers were conducted, and the meaning of expertise was explored, focusing on play that showed the teacher's expertise well. First, in becoming a snail, the curriculum execution ability to maintain a long breath of play and the ethics of each other's responsibility appeared. Next, it was found that watering dogs and stepping stones that attract children have a sensitivity to learning that teachers who apply the curriculum to children who meet physical properties are everywhere. Finally, the flowing rainbow scarf play showed a sense of recognizing the flow of play and flexibility in the action of power in the classroom. Based on this, it was discussed that not only intellectual but also practical skills work together, so experience is needed to improve teachers' practical expertise, that teachers' learning is an internal-action with ontological and ethical characteristics, and that personal efforts and educational conditions should be improved.
Factors Influencing Adolescents' Illegal Filming Behavior: A Focus on Adolescents in the Busan Area

This study was conducted to empirically investigate the factors influencing the serious societal issue of illegal filming. Using data from the “Survey on the Sexual Culture of Children and Adolescents in the Busan Area” conducted by the Busan Women and Family Development Institute in 2021, the study analyzed the factors that influence adolescents' involvement in illegal filming. A total of 853 participants were included in the study, and binary logistic regression analysis was used for the analysis. The results of the study indicated that the factors statistically significant in influencing adolescents' engagement in illegal filming were the experience of encountering sexual discrimination expressions online and a lack of recognition of illegal filming as a criminal activity. Based on the findings, this study recommended the development of practical measures to address sexual discrimination in online environments targeted at adolescents, the enhancement of digital sexual violence prevention education.
An Analysis of Cohesive Devices on Phoenix Posts on The Death of Queen Elisabeth II

This paper explores some cohesive devices namely reference, repetition, and collocation on Phoenix messages by internet users worldwide in reaction to the death of Queen Elizabeth II of the United Kingdom. 400 e-messages on Phoenix platform were collected from 175 informants. Each participant provided at most three e-messages. The study examines the types of cohesive devices and the extent to which they are used in the various conversations of the texters. Insights were drawn from Halliday and Hassan (1976) who laid down the foundation of cohesion theory in English. They identified two different types of cohesive devices namely grammatical and lexical cohesion. The grammatical type is made up of reference, conjunction, substitution, and ellipsis; while lexical one comprises reiteration and collocation. Facts from the analysis of the data revealed that reference was the dominant cohesive devices. More precisely, the demonstrative pronouns (28.57%) are the most used referential items. it is seconded by possessive pronouns (23.42%) and the least employed devices is personal pronouns (20.57%). The results also indicated the use of repetition and collocation in messages of the participants. The most repeated word is Queen while the least reduplicated one is slave/slavery. Items of collocation are used at varying frequencies.
Investigation of the Correlation Between the Visually Induced Motion Sickness Susceptibility Questionnaire and the Turkish Motion Sickness Susceptibility Questionnaire

Visually induced motion sickness (VIMS) is a phenomenon similar to motion sickness frequently observed in users of visual technologies. The Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ), developed by Golding et al. (2006), is considered the most effective scale for assessing VIMS susceptibility levels. The main purpose of this study was to standardize the selection of participants for research conducted with virtual reality, especially motion sickness (MS) research. To achieve this, first, the Turkish version of the VIMSSQ was created to establish its validity and reliability, and subsequently, its correlation with the Turkish Motion Sickness Susceptibility Questionnaire Short form (HDDA), the expanded version of the Motion Sickness Susceptibility Questionnaire Short form (MSSQ), was examined. Subjects and.Linguistic equivalence assessment was obtained from ten experts by passing the VIMSSQ through the translation process. The VIMSSQ and the Turkish MSSQ forms were then administered to 49 subjects. This study statistically analyzed the validity and reliability of the VIMSSQ and its relationship with the MSSQ.Results showed that the Turkish version of the original questionnaire is highly reliable (Cronbach alpha=0.843). There is a moderate statistically significant positive correlation between the total MSSQ scores and the subfactors of the VIMSSQ.In this study, VIMSSQ was successfully adapted to Turkish, normative data demonstrated its validity, and all sub-factors were highly reliable. The Turkish version of the VIMSSQ can serve as a valuable tool for estimating individual susceptibility to VIMS.
Chinese relation extraction in military field based on multi-grained lattice transformer and imbalanced data classification

Relation extraction (RE) is a crucial step for knowledge graph construction, which aims to extract meaningful relations between entity pairs in plain texts. Very few works have been studied on Chinese relation extraction (CRE) in the military field. Moreover, recent deep neural network-based methods have achieved considerable performance but still suffer from three inherent limitations, including overlapping of entities, imbalanced data and the ambiguity. Therefore, this work investigates a novel Multi-Grained Lattice Transformer (MGLT), which leverages external information of lexicon and word sense tailored for CRE. In MGLT, self-matched lexicon words and related word senses are fused through a cross-transformer mechanism to alleviate the ambiguity in texts. The finally enriched sequence representation in MGLT captures the relatedness between the head entity and the tail one, which is helpful to alleviate the overlapping of entities. Experimental results on two benchmark datasets and a self-developed dataset constructed from online military news show that the proposed MGLT achieves state-of-the-art (SOTA) performance. Compared with other typical baselines, MGLT achieves better area under curve (AUC) and [Formula: see text]-score by up to 10.46% and 6.90%, respectively. We further demonstrate the effectiveness of using ensemble learning to fully exploit complementary information from multiple MGLT-based base learners to improve the overall performance for imbalanced data classification on the military dataset. Such results indicate that the proposed ensemble learning model is effective and robust to be applied in practical applications.
Deep learning-based framework for quantification and prognostic of polyploid giant cancer cells

Primary liver cancer is one of the most lethal cancers worldwide. Polyploid giant cancer cells seen on pathological examination are associated with prognosis of it. However, its quantification requires doctors to interpret region by region on pixel-level images, which is time-intensive and subjective, not been applied clinically yet. For the first time, we proposed the PGCCI index based on the quantity of polyploid giant cancer cells and the size of the tumor region, developed a deep learning based PGCCdet framework for detection and calculation, and proved the validity of PGCCI by survival analysis. In our framework, U-Net and Resnet are used for foreground segmentation and tissue classification respectively. For polyploid giant cancer cells detection, we proposed YOLOCN, a detection network combining multi-scale feature fusion and multi-dimensional attention mechanism, to achieve accurate quantification. For survival analysis, Kaplan-Meier method is implemented. Experiments show that on the self-owned HCCP dataset, the F1 score of detection is 0.814 and the map@0.5 is 0.871, better than other methods. Survival analysis on the TCGA dataset with 323 cases shows that the median survival time of the low PGCCI group was 84.4 months, while the high PGCCI group was 38.3 months. The Pvalue was smaller than 0.0001, indicating a significant survival difference. The results show our framework can accurately detect polyploid giant cancer cells and the PGCCI can effectively predict survival. This framework can change the way of diagnosis of liver cancer with great application potential in the clinical diagnosis and treatment in the future.
A word's distribution of reported results prediction model based on BP neural network

In recent years, COVID-19 has brought a huge impact on people’s life, making online activities a new trend, which promotes the rapid development of mini-games. A mini-game called “Wordle” came out currently has been brought into focus. Players are required to solve the puzzle by guessing a five-letter word in six tries or less, receiving feedback with every guess. In this paper, we use BP neural network to predict the distribution of reported results for a given solution word on a certain future date (March 1, 2023 as an example) by using the daily data from January 7, 2022 to December 31, 2022. The empirical result shows that the distribution of reported results will be (1: 10%, 2: 15%, 3: 35%, 4: 25%, 5: 10%, 6: 5%, X: 0%). Besides, the R-values of the four sets of regression data are 0.92895, 0.904, 0.89493 and 0.9199 respectively, which shows that it is effective to use BP neural network to predict the distribution of reported results.
A BWCNN-GRU-LSTM-ATT model-based sentiment classification method for civil aviation passengers

To address the problems that a single Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cannot determine the importance of each word in the text when extracting features from the text and the high time complexity of the network, this paper proposes a text classification method based on a multi-channel This paper proposes a text classification model (BWCNN-GRU-LSTM-ATT) based on multi-channel CNN, Gated Recurrent Unit Network (GRU), LSTM and Attention mechanism (ATT) to solve the problem of inadequacy of a single method, so as to achieve accurate classification of civil aviation passengers' emotions. Firstly, we use word embedding to vectorize the input text, extract the local features of the text through multi-channel CNN, and then integrate the full text semantics; make full use of the advantages of GRU and LSTM, and design a GRU-LSTM tandem network model to increase the effective extraction of contextual semantic information; then increase the weight of keywords by adding the attention mechanism to fully extract the features; finally, we adopt the Bayesian optimization algorithm is adopted to optimize the model parameters and construct the optimized sentiment classification of civil aviation passengers. The results show that the classification accuracy reaches 99.2% and achieves better classification results.
Speech emotion recognition based on ResNet-BiGRU network

In order to improve the anthropomorphic nature of intelligent speech products, the academic research on speech emotion recognition is getting hotter and hotter. Currently, the speech emotion recognition system mainly consists of two steps: speech feature extraction and speech feature classification. In order to improve the accuracy of speech emotion recognition, the Mel Frequency Cepstrum Coefficient (MFCC) of speech signal, which has a good effect on the feature capability in the field of speech at this stage, is chosen as the input of the deep learning network, and the ResNet-BiGRU network based on the attention mechanism is used to extract the MFCC information is extracted using ResNet-BiGRU network based on the attention mechanism. The experimental results show that the introduction of attention mechanism in the model can effectively focus on useful information and reduce the interference of redundant information. The accuracy rate on the Chinese sentiment corpus CASIA reached 84.83%.
Implementation of a low-code monitoring system for solid oxide fuel cells

Solid oxide fuel cells (SOFCs) have the advantages of high conversion efficiency, low working noise, and environmental protection. Thus, they are widely used in large-scale power generation, combined heat power, peak load-regulating energy storage, and other fields. In the actual working process of SOFCs, the battery performance will decline sharply due to extreme conditions such as overtemperature and peroxide, and the service life will be terminated. Remote monitoring of fuel cells can detect problems in time and restore the battery’s performance by restoring the battery, thereby extending its service life. The data generated by fuel cells during operation have a strong temporal correlation. To discover the future state of fuel cells in time and solve the problem that a large number of codes are needed for monitoring, this paper designs a remote monitoring system of fuel cells with low-code based on Node-RED and Ali Cloud. The output current of fuel cells is predicted by using the improved back propagation neural network based on Levenberg-Marguardt (LM-BPNN) algorithm. The test result of LM-BPNN shows that the test samples have a root mean square error of 0.172, a mean absolute percentage error of 3.85%, and a coefficient of determination of 0.998.
Contrastive learning for stance detection based on theme

Stance detection estimates the stance of a user towards a specific target through the text that the user posts. As an important step in public-opinion mining, stance detection has great potential for wide application and provides necessary data for decision-making by governments and enterprises. On social media, people exchange ideas by replies, in which the texts of replies are closely related to the context. Existing works in this regard focus mainly on the method of aggregating the context, but overlook the fact that the theme of texts alters with the progress of the response. Therefore, we propose a stance detection model that can effectively learn the thematic information, where contrastive learning is used to enhance the learning effect of thematic information. Experiments were performed on CreateDebate and Kialo datasets, and our proposed model achieved an accuracy of 68.0% and 69.6% on the two datasets, respectively. Experiments have proved that our method can improve the performance of stance detection.
Multi-label test classification based on capsule network and virtual adversarial training

Based on capsule network and virtual adversarial training, a multi-label text classification method is proposed for the multi-label text classification problem to solve the problems of insufficient feature extraction and weak model robustness. This method used BERT model to obtain the word vector representation of the sample and used the dynamic routing mechanism of capsule network to extract text features. At the same time, the method added disturbance construction adversarial samples into the input samples, and updated the model parameters by virtual adversarial training method, finally, improved robustness and classification effect of the model. Experiments on AAPD and RCV1-V2 datasets show that the micro-F1 value is better than those of other benchmark models. Experimental results show that the proposed method can be effectively applied to multi-label text classification tasks.
Hot spot detection method of air preheater based on chaos theory and cluster analysis

The temperature data of air preheater contains a lot of system state information, but it is difficult to extract effective features from the temperature time series signals to monitor the running state of air preheater. In this paper, a new hot spot detection method for air preheater is proposed based on chaos theory and clustering anomaly detection method. Firstly, the phase space of temperature time series is reconstructed by using chaos theory to restore the system dynamics characteristics. Then, combined with the actual operating conditions of air preheater, the reconstructed data are analyzed by clustering to get the characteristic class of hot spot, so as to realize the hot spot detection of air preheater. The simulation results show that the clustering anomaly detection method is feasible in the hot spot detection of air preheater, and can effectively identify the hidden danger of abnormal temperature change of air preheater. The research results provide a theoretical basis for detecting the hot spot hidden trouble of air preheater from the perspective of chaos mechanics