Reviewerly/exHarmony huggingface dataset:
Classification and topic tracking of college students’ cybersecurity education based on the internet

Campus security issues have always been a hot topic of concern from all walks of life. To analyze the changes in network public opinion of college students’ campus security incidents, this study proposes to classify campus security incidents and track related topics. The classification algorithm and topic tracking algorithm for campus security incidents based on Internet technology are studied, and a prototype system of campus security incidents based on Internet is designed for analysis. The research results show that the Internet-based campus security incident prototype system can classify campus security incidents more accurately, and can also track security incidents on related topics, which provide support for student security management to a certain extent. This technology will be applied to the tracking and classification of college safety accidents, and provide certain reference for ensuring college students’ campus safety.
Streaming-Based Anomaly Detection in ITS Messages

Intelligent transportation systems (ITS) enhance safety, comfort, transport efficiency, and environmental conservation by allowing vehicles to communicate wirelessly with other vehicles and road infrastructure. Cooperative awareness messages (CAMs) contain information about vehicles status, which can reveal road anomalies. Knowing the location, time, and frequency of these anomalies is valuable to road users and road authorities, and timely detection is critical for emergency response teams, resulting in improved efficiency in rescue operations. An enhanced locally selective combination in parallel outlier ensembles (ELSCP) technique is proposed for data stream anomaly detection. A data-driven approach is considered with the objective of detecting anomalies on the fly from CAMs using unsupervised detection approaches. Based on the experiments carried out, we note that ELSCP outperforms other techniques, with 3.64 % and 9.83 % better performance than the second-best technique, LSCP, on AUC-ROC and AUCPR, respectively. Based on our findings, ELSCP can effectively detect anomalies in CAMs.
Analyzing the spatiotemporal coupling relationship between public opinion and the epidemic during COVID-19

Purpose The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies. Design/methodology/approach This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space. Findings The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages. Originality/value This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.
The impact of Leagile software development on operational performance

Purpose In software development (SD), practitioners have realized the importance of lean thinking. A new term “Leagile” is coined, which is an integrated approach of traditional lean and agile thinking to managing the operations. The study aims to investigate the application of the Leagile principles in a new sector and establish a relationship between the adoption of Leagile approach and operational performance (OP) in SD organizations. Design/methodology/approach Empirical research is conducted to investigate the linkage between the Leagile principles and operational measures. Data is collected through surveys from 256 SD industries located in 11 states of India and analysed using the structural equation modelling approach. Findings The practitioners envisage a positive impact of adoption of Leagile principles on OP of SD organizations, but one of the principles, i.e. perfection, is unable to influence the performance. Originality/value The study contributes by authorizing the contribution of Leagile principles towards OP of SD organizations. The outcomes will motivate the practitioners to enhance the adoption of Leagile principles in SD organizations.
Improving part-of-speech tagging in Amharic language using deep neural network

<h2>Abstract</h2> To date, several POS taggers have been introduced to facilitate the success of semantic analysis for different languages. However, the task of POS tagging becomes a bit intricate in morphologically complex languages, like Amharic. In this paper, we evaluated different models such as bidirectional long short term memory, convolutional neural network in combination with bidirectional long short term memory, and conditional random field for Amharic POS tagging. Various features, both language-dependent and -independent, have been explored in a conditional random field model. Besides, word-level and character-level features are analyzed in deep neural network models. A convolutional neural network is utilized for encoding features at the word and character level. Each model's performance has evaluated on the dataset that contained 321 K tokens and manually tagged with 31 POS tags. Lastly, the best performance obtained by an end-to-end deep neural network model, convolutional neural network in combination with bidirectional long term short memory and conditional random field, is 97.23% accuracy. This is the highest accuracy for Amharic POS tagging task and is competent with contemporary taggers currently existing in different languages.
The construction of the inheritance performance evaluation system for inheritors of intangible cultural heritage of traditional sports

Abstract This paper shows the construction of a performance evaluation system for the inheritance of traditional sports intangible cultural inheritors. In order to ensure the scientific nature and rationality of the indicators, a theoretical model of performance evaluation content is first established. Then, based on the theoretical model, the preliminary selection of indicators is completed, and then the expert screening of indicators is further completed by Delphi method, and the weights of each indicator are established through analytic hierarchy. Finally, in order to verify the application effect of the indicator system in practice, this study conducts an empirical study on the system from the perspective of regional performance evaluation. The results show that the discriminating power and applicability of the evaluation indicator are effective, and the evaluation system has a very distinct epochal and people-centered character. In conclusion, the construction of this evaluation system is of great significance to improve the scientific and standardized level of evaluation of Intangible cultural heritage inheritors.
A two-stage data-model driven pancreas segmentation strategy embedding directional information of the boundary intensity gradient and deep adaptive pointwise parameters

Abstract In this paper, we propose a two-stage data-model driven pancreas segmentation method that combines a 3D convolution neural network with adaptive pointwise parametric hybrid variational model embedding the directional and magnitude information of the boundary intensity gradient. Firstly, nnU-net is used to segment the entire abdominal CT image with the aim of obtaining the region of the interest of pancreas. Secondly, an adaptive pointwise parametric variational model with a new edge term containing the directional and magnitude information of the boundary intensity gradient is used to refine the predicted results from CNN. Although CNN is good at extracting texture information, it does not capture weak boundary information very well. In order to well acquire more weak boundary information of the pancreas, we utilize not only the magnitude of the gradient, but also the directional information of the boundary intensity gradient to obtain more accurate results in the new edge term. In addition, the probability value for each pixel obtained by calculating the softmax function is exploited twice. Actually, it is applied firstly to generate the binary map as the initial contour of the variational model and then to design the adaptive pointwise weight parameters of internal and external area terms of the variational model rather than constants. It not only eliminates the trouble of manual parameter adjustment, but also, most importantly, provides a more accurate pointwise evolutionary trend of the level set contour, i.e. determine the tendency of the level set contour to pointwisely contract inward or expand outward. Our method is evaluated on three public datasets and outperformed the state-of-the-art pancreas segmentation methods. Accurate pancreatic segmentation allows for more reliable quantitative analysis of local morphological changes in the pancreas, which can assist in early diagnosis and treatment planning.
Trends of Research Keywords Related to the Network and Negotiating Skills in Digital Era: A Bibliometric Review

Objective: This study had a purpose to know the trend research in Scopus indexed papers related to the networking and negotiating. Theoretical framework: This study used examined the research from SCOPUS indexed journal. It showed that based on the author’s keyword, keywords like innovation, leadership, and network were really intertwined with each other. Method: There were 697 samples found by typing networking and negotiate* in Scopus search. The data were analyzed using bibliometric method and processed using Vosviewer. Results and conclusion: The result showed that based on the authors keyword, keywords like innovation, leadership, and network were really intertwined with each other. Implications of the research: Networking and negotiating were popular topics in those two countries, so that many scholars and researchers can observe more about negotiation and networking. Originality/value: This research concluded that the author keywords like innovation, leadership, and network were really intertwined with each other. Based on index keywords, humans are connected to other keywords, such as adult, skills, organization and management, leadership, and program evaluation.
Attention-Based Mechanisms for Cognitive Reinforcement Learning

In this paper, we propose a cognitive reinforcement learning method based on an attention mechanism (CRL-CBAM) to address the problems of complex interactive communication, limited range, and time-varying communication topology in multi-intelligence collaborative work. The method not only combines the efficient decision-making capability of reinforcement learning, the representational capability of deep learning, and the self-learning capability of cognitive learning but also inserts a convolutional block attention module to increase the representational capability by using the attention mechanism to focus on important features and suppress unnecessary ones. The use of two modules, channel and spatial axis, to emphasize meaningful features in the two main dimensions can effectively aid the flow of information in the network. Results from simulation experiments show that the method has more rewards and is more efficient than other methods in formation control, which means a greater advantage when dealing with scenarios with a large number of agents. In group containment, the agents learn to sacrifice individual rewards to maximize group rewards. All tasks are successfully completed, even if the simulation scenario changes from the training scenario. The method can therefore be applied to new environments with effectiveness and robustness.
Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records.

Recently, hospitals and healthcare providers have made efforts to reduce surgical site infections as they are a major cause of surgical complications, a prominent reason for hospital readmission, and associated with significantly increased healthcare costs. Traditional surveillance methods for SSI rely on manual chart review, which can be laborious and costly. To assist the chart review process, we developed a long short-term memory (LSTM) model using structured electronic health record data to identify SSI. The top LSTM model resulted in an average precision (AP) of 0.570 [95% CI 0.567, 0.573] and area under the receiver operating characteristic curve (AUROC) of 0.905 [95% CI 0.904, 0.906] compared to the top traditional machine learning model, a random forest, which achieved 0.552 [95% CI 0.549, 0.555] AP and 0.899 [95% CI 0.898, 0.900] AUROC. Our LSTM model represents a step toward automated surveillance of SSIs, a critical component of quality improvement mechanisms.
Egocentric Hierarchical Visual Semantics

We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an object associated with a certain concept. The ultimate goal is to build systems which can meaningfully interact with their users, describing what they perceive in the users’ own terms. As from the field of Lexical Semantics, humans organize the meaning of words in hierarchies where the meaning of, e.g., a noun, is defined in terms of the meaning of a more general noun, its genus, and of one or more differentiating properties, its differentia. The main tenet of this paper is that object recognition should implement a hierarchical process which follows the hierarchical semantic structure used to define the meaning of words. We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia. In other words, the recognition of an object is decomposed in a sequence of steps where the locally relevant visual features are recognized. This paper presents the algorithm and a first evaluation.
Data Protection based on Searchable Encryption and Anonymization Techniques

Data leakage compromises companies’ confidentiality and directly impacts the existing privacy laws, as well as it is necessary to perform a light integration with the legacy systems, in order to not harm the performance of its services. Within this context, this paper presents an innovative cloud system to protect the private data of existing databases (legacy systems of clients) based on Searchable Symmetric Encryption for Databases (SSEDB) and Permutation and Proprieties Maintenance Anonymization (PPM-Anon), attaching a security solution to the existing databases (without any change in these legacy systems). Results from real experiments using a real cloud environment suggest that the proposed solution is suitable for protecting the data without harming the performance of the existing services.
Nuclei Segmentation Approach for Computer Aided Diagnosis

Computer aided diagnosis based on computational pathology combines the concepts of pathology with computer science to develop automated mechanisms for interpretation of histological images. Nuclei segmentation is a type of computation pathology that enables the identification and separation of Nuclei cells in a histopathology image. These segmentation routines play a critical role in quantification of cellular structures and provide insights to identify abnormalities related to pathological conditions such as cancer. In order to determine the progress and severity of a pathological condition it is necessary to use tools that measure the properties of tissue structure in different stages of a disease. Biomedical image analysis for Nuclei segmentation using deep learning methodologies is an ongoing area of research wherein to improve histopathology examination it is desired to develop a model that can generalize and accurately segment different types of nuclei. However, creating a suitable model for this process is difficult due to variability in patient data, overlapping nuclei cell boundaries, image quality, background noise, and provision of well-annotated data. In this study, we describe our findings on the performance of a model developed using the standard U-Net architecture and compare it against a model that is developed based on pre-trained weights and models using ImageNet as the backbone. The aim is to develop a semantic segmentation model that can capture high-level information in terms of features from a data set that consists of Whole-slide images (WSI). The outcome of the research is to be able to select a semantic segmentation model that is efficient and fast to classify Nuclei cells for the given biomedical tissue images. The model uses a stochastic gradient descent optimizer and the evaluation criteria for this network i.e., the ground truth against prediction is based on Mean Intersection over Union.
Complete and fidelity-robust state analysis for polarization-spatial-time-bin hyperentanglement with double-sided quantum-dot-cavity systems

Hyperentanglement represents a key resource in many quantum information processing schemes. Here, we present a complete and fidelity-robust hyperentangled-Bell-state analysis (HBSA) scheme for two-photon systems entangled in polarization, spatial mode, and time-bin degrees of freedom (DOFs). The scheme uses a fidelity-robust quantum nondemolition (QND) detector called BLOCK1, built with a singly charged semiconductor quantum dot (QD) in a double-sided optical microcavity (double-sided QD-cavity system) under the balance condition, and some linear-optical elements. Compared with the previously proposed complete HBSA schemes based on ideal optical giant circular birefringence, our scheme guarantees the robust fidelity and relaxes the requirement on the QD-cavity parameters with the assistance of the balance condition. Different from the schemes using error-detected blocks and exchanging Bell states in different DOFs to get unity fidelity, the BLOCK1 under the balance condition can directly measure the Bell states of polarization and spatial mode so that fewer kinds of linear optical components are needed in our scheme. In addition, our scheme simplifies the discrimination process and reduces the required light–matter interaction by using a self-assisted mechanism. These features indicate that our scheme may be directly generalized to multiphoton hyperentangled Greenberger–Horne–Zeilinger state analysis and more feasible in practical quantum applications based on the photonic hyperentanglement.
Developing future managers through business simulation gaming in the UK and Hong Kong: exploring the interplay between cognitive realism, decision-making and performance

Purpose This paper investigates how individuals' decision-making approach and perceptions of a game's cognitive realism affect the performance of virtual businesses in a web-based simulation game. Design/methodology/approach Survey data are collected from 274 business simulation game users and is analysed using the fsQCA technique. Findings The study identifies three alternative pathways to high and low performance in a business simulation game. Results indicate that a flexible decision-making approach exists in all high performance pathway solutions. Where a game is perceived to be realistic, a more focused decision-making approach is associated with high performance. However, where perceived cognitive realism is absent, a less focused experimental decision-making approach is employed, which increases the chances to achieve low performance. Finally, perceived cognitive realism and an experimental decision-making approach are found to be mutually exclusive for achieving high performance. Originality/value Whilst the learning benefits of web-based simulation games are widely acknowledged, the complex interplay amongst factors affecting performance in games is under-researched. Limited research exists on how perceptions of a game's cognitive realism interact with user decision-making approaches to affect performance.
CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks.
YOLO Algorithms Performance Comparison for Object Detection in Adverse Weather Conditions

This paper aimed to evaluate and compare the performance of various versions of the YOLO object detection (OD) algorithm under adverse weather conditions, including rain, fog, snow, and sand, during both day and night. A dataset of images captured in these conditions was used to carry out the performance, in terms of OD, of the previous last five versions of YOLO (You Only Look Once), using a cloud-based platform, named Google Colab Notebook. The obtained results showed that YOLOv7 outperformed the other versions in terms of both speed and accuracy. It was the fastest algorithm, completing OD in under 17.4 milliseconds, and it had the highest detection rate for the most classes in the dataset. These findings suggest that YOLOv7 is the best option for OD in adverse weather conditions and under challenging lighting conditions.