Reviewerly/exHarmony huggingface dataset:
The influence of organizational values on employee attitude and information security behavior: the mediating role of psychological capital

Purpose With increased remote working, employers are concerned with employees’ commitment and compliance with security procedures. Through the lens of psychological capital, this study aims to investigate whether strong organizational values can improve employees’ commitment to the organization and security behaviors. Design/methodology/approach Using Qualtrics platform, the authors conducted an online survey. The survey participants are college-educated, full-time employees. The authors used structural equation modeling to analyze 289 responses. Findings The results indicate perceived importance of organizational values is associated with increased organizational commitment and information security behavior. The authors find that psychological capital partially mediates these relations suggesting that employees’ psychological capital effectively directs employees toward an affinity for the organization and information security behavior. The results highlight the importance of organizational values for improving security behavior and organizational commitment. Second, the results suggest that psychological capital is an effective mechanism for this influence. Finally, the authors find that individual differences (gender, organizational level and education) are boundary conditions on their findings, providing a nuanced view of their results and offering opportunities for further investigation. Originality/value To the best of the authors’ knowledge, this study is the first to explore organizational values in relation to information security behaviors. In addition, this study investigates the underlying mechanism of this relationship by showing psychological capital’s mediating role in this relationship. Therefore, the authors suggest organizations create a supportive environment that appreciates innovation, quality services, diversity and collaboration. Furthermore, organizations should communicate the importance of these values to their employees to motivate them to have a stronger affective commitment and a more careful set of security behaviors.
Social media engagement and cryptocurrency performance

Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies' existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months.
Analysis of alternative strategies applied to Naïve-Bayes classifier into the recognition of electrofacies: Application in well-log data at Recôncavo Basin, North-East Brazil

This paper is concerned with the applicability of different strategies to improve the definition of prior probabilities and/or likelihoods of naïve Bayes (NB) classifiers. Standard NB method computes likelihoods and priors by means of normal distributions and evaluation of the entire training data-set, respectively. NB is one of the most prolific classification methods in data mining and machine learning. Despite decent efficiency facing good training data, NB classifiers present the intriguing assumption of conditional independence between the attributes. Several algorithms have been proposed to improve the effectiveness of NB classifiers by inserting discriminant approaches into its generative structure. From a reliability perspective, the standard NB approach might not explore the real capabilities of NB classifiers facing the lithologic classification problem. To cover such distrust, a novel approach considering four particular strategies are suggested and compared to the standard NB classification outcomes. At first, a kernel density estimation (KDE) is considered to ameliorate the likelihood models. We also apply the NB classifier in parts by separating the training data-set in individual wells in a committee architecture framework. A tuning strategy is also considered for automatic estimation of prior probabilities in an optimization-scheme. Another novel alternative, named as CRC, adapted to the standard NB classifier consists in defining priors based on depth zones from regional stratigraphic information in which to apply NB classifier. We prepare an extensive statistical investigation, based on precision, recall, classification errors, fscores and confusion matrices to bespeak the most relevant NB strategy for classification of electrofacies. Despite the decent classification outcomes for all above-mentioned strategies, CRC can be considered, by a narrow margin, the most prolific method to be applied as an improvement of the standard NB to classify rock units. Tests are performed on a validation well (i.e., 7-MP-50D-BA) of Massapê Field, in Recôncavo Basin, northeast Brazil to highlight the classification particularities provided by the improved strategy. A significant improvement in the classification of sandstones (i.e., from 68 % to 83 % accuracy) is observed, according to the confusion matrix analysis. Additionally, a minimal decreasing is observed into the classification of shales and slurries (i.e., from 92% to 90% and to 63% to 56%, respectively), which is acceptable according to the fscores and errors. This aspect reinforces the relevance of using the NB classifier jointly with previous geologic information to optimize the lithologic classification.
Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream

The imbalance and concept drift problems in data streams become more complex in multi-class environment, and extreme imbalance and variation in class ratio may also exist. To tackle the above problems, Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm deals with imbalance and concept drift problems through the hybrid sampling and dynamic weighting phases, respectively. In the hybrid sampling phase, adaptive spectral clustering is proposed to sample the data after clustering, which can maintain the original data distribution; then the sample safety factor is used to determine the samples to be sampled for each class; the safe samples are oversampled and the unsafe samples are under-sampled in each cluster. If the data stream is extremely imbalanced, the sample storage pool is used to extract samples with a high safety factor to add to the data stream. In the dynamic weighting phase, a dynamic weighting method based on the G-mean value is proposed. The G-mean values are used as the weights of each base classifier in the ensemble and the ensemble is dynamically updated during the processing of the data stream to accommodate the occurrence of concept drift. Experiments were conducted with LB, OAUE, ARF, BOLE, MUOB, MOOD, CALMID, and the proposed HSDW-MI on 10 multi-class synthetic data streams with different class ratios and concept drifts and 3 real multi-class imbalanced streams with unknown drifts, and the results show that the proposed HSDW-MI has better classification capabilities and performs more consistently compared to all other algorithms.
Dual-branch vision transformer for blind image quality assessment

Blind image quality assessment (BIQA) has always been a challenging problem due to the absence of reference images. In this paper, we propose a novel dual-branch vision transformer for BIQA, which simultaneously considers both local distortions and global semantic information. It first extracts dual-scale features from the backbone network, and then each scale feature is fed into one of the transformer encoder branches as a local feature embedding to consider the scale-variant local distortions. Each transformer branch obtains the context of global image distortion as well as the local distortion by adopting content-aware embedding. Finally, the outputs of the dual-branch vision transformer are combined by using multiple feed-forward blocks to predict the image quality scores effectively. Experimental results demonstrate that the proposed BIQA method outperforms the conventional methods on the six public BIQA datasets.
Emerging uses of artificial intelligence in breast and axillary ultrasound

Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also plays an important role in staging breast cancer by assessing axillary lymph nodes. However, its utility is limited by operator dependence, high recall rate, low positive predictive value and low specificity. These limitations present an opportunity for artificial intelligence (AI) to improve diagnostic performance and pioneer novel uses of ultrasound. Research in developing AI for radiology has flourished over the past few years. A subset of AI, deep learning, uses interconnected computational nodes to form a neural network, which extracts complex visual features from image data to train itself into a predictive model. This review summarizes several key studies evaluating AI programs' performance in predicting breast cancer and demonstrates that AI can assist radiologists and address limitations of ultrasound by acting as a decision support tool. This review also touches on how AI programs allow for novel predictive uses of ultrasound, particularly predicting molecular subtypes of breast cancer and response to neoadjuvant chemotherapy, which have the potential to change how breast cancer is managed by providing non-invasive prognostic and treatment data from ultrasound images. Lastly, this review explores how AI programs demonstrate improved diagnostic accuracy in predicting axillary lymph node metastasis. The limitations and future challenges in developing and implementing AI for breast and axillary ultrasound will also be discussed.
Class-specific feature selection via maximal dynamic correlation change and minimal redundancy

Information theory has been widely used to evaluate the relevance and redundancy of features in feature selection. The traditional feature selection methods based on information theory focus on classification-specific feature selection. Classification-specific feature selection considers all classes together and therefore may not be able to select a desirable feature subset for a particular class. In contrast, class-specific feature selection can choose an appropriate feature subset for each class. The main goal of this paper is to propose a class-specific feature selection method based on information theory. In this regard, we first develop a class-specific feature evaluation criterion called Class-Specific Maximal Dynamic Correlation Change and Minimal Redundancy (CSMDCCMR), which consists of three items. The first item characterizes the correlation between a candidate feature and a particular class. The second item quantifies the dynamic changes in the relevance of a candidate feature and selected features with respect to a particular class. The third item expresses the redundancy between a candidate feature and selected features with respect to a particular class. We then design a class-specific feature selection algorithm by combining the CSMDCCMR criterion with a sequential forward search strategy, which can be employed to select a suitable feature subset for each class. Finally, we carry out extensive experiments to compare our class-specific feature selection method with nine representative classification-specific feature selection methods based on information theory and six state-of-the-art class-specific feature selection methods. The experimental results show that our class-specific feature selection method achieves better classification performance than these comparison methods.
SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNet

Pseudo-relevance feedback is a widely acclaimed technique for information retrieval. However, traditional information retrieval approaches typically process the original query into individual terms, often overlooking the consideration of the semantic information in the query term itself, by instead focusing on features such as term frequency and inverse document frequency. In this paper, we propose a new semantic-based Pseudo-relevance Feedback model (SPRF) based on the PRF framework. Our SPRF model leverages ConceptNet to provide comprehensive semantic information between terms. It not only considers the query's importance in the collection but also integrates its semantic information into the PRF framework to enhance the selection of query expansion terms, leading to more precise feedback documents for users. A series of experimental results show that our proposed SPRF model is feasible. Our model achieves good performance in terms of the MAP, P@10, NDCG and MRR metrics and demonstrates advantages over the baseline models, the state-of-the-art models and several neural network-based methods. By comparing and analyzing the methods in a sample case, the extended terms resulting from the proposed model are shown to be in better semantic agreement with the given query.
Multi-Task Reinforcement Learning With Attention-Based Mixture of Experts

Multi-task learning is an important problem in reinforcement learning. Training multiple tasks together brings benefits from the shared useful information across different tasks and often achieves higher performance compared to single-task learning. However, it remains unclear how parameters in the network should be reused across tasks. Instead of naively sharing parameters across all tasks, we propose an attention-based mixture of experts multi-task reinforcement learning approach to learn a compositional policy for each task. The expert networks learn task-specific skills which specialize in different parts of multi-task representation space. To assemble the expert outputs, we propose an attention module to generate connections between tasks and experts to achieve the best performance automatically. The proposed approach can effectively learn task relationships from soft attention weights and alleviate mutual interference through independent expert networks. We use the proposed approach to improve both sample efficiency and overall performance over baseline algorithms in Meta-world, a benchmark for multi-task reinforcement learning containing 50 robotic manipulation tasks, and the multi-task MUJOCO environment, which contains four considerably different tasks with diverse state transition dynamics.
HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning

The increasing data produced by IoT devices and the need to harness intelligence in our environments impose the shift of computing and intelligence at the edge, leading to a novel computing paradigm called Edge Intelligence/Edge AI. This paradigm combines Artificial Intelligence and Edge Computing, enables the deployment of machine learning algorithms to the edge, where data is generated, and is able to overcome the drawbacks of a centralized approach based on the cloud (e.g., performance bottleneck, poor scalability, and single point of failure). Edge AI supports the distributed Federated Learning (FL) model that maintains local training data at the end devices and shares only globally learned model parameters in the cloud. This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL, which supports a sustainable learning paradigm using model parameters aggregation at different layers and considering adaptive learning rounds at the edge to save energy but still preserving the learning model's accuracy. Performance evaluations of the proposed approach have also been led out considering model accuracy, loss, and energy consumption.
HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time–frequency representation

Human activity recognition (HAR) based on wearable devices has progressively advanced in context-aware computing like healthcare, smart homes, and industry 4.0. Since machine learning and Internet-of-Things have been in the spotlight, many deep learning-based approaches have been proposed, achieving very good performance using inertial data. In this study, we explore different joint time–frequency representations of the sensor data, in conjunction with a Convolutional Neural Network (ConvNet) to implement activity classification. We also consider the concurrent use of two different data representations in a cooperative bi-stream ConvNet configuration. Scaling of joint time–frequency representation is explored to reduce the ConvNet model size, and hence the computational complexity of the proposed approach. The classification performance and energy efficiency of the proposed method are evaluated on a real-world public HAR dataset. The performance of pairwise combinations and also, the single-stream of the two prominent representations in the joint time–frequency domain are compared to that of the state-of-the-art. The proposed method is implemented on Google's Edge TPU platform to investigate the trade-off between the energy footprint and classification performance, which is critical in IoT applications where machine learning inference needs to be performed on resource-constrained edge devices. The highest overall classification accuracy and F1 score achieved by our method was 98.39% and 96.86%, respectively, which represents a significant improvement over the state-of-the-art.
Identifying the Mutual Correlations and Evaluating the Weights of Factors and Consequences of Mobile Application Insecurity

Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and tools for ensuring the security of mobile applications, on the other hand. Therefore, ensuring the security of mobile applications by developing effective methods and tools is a challenging task today. This study aims to evaluate the mutual correlations and weights of factors and consequences of mobile application insecurity. We have developed a method of evaluating the weights of factors of mobile application insecurity, which, taking into account the mutual correlations of mobile application insecurity consequences from these factors, determines the weights of the factors and allows us to conclude which factors are necessary to identify and accurately determine (evaluate) to ensure an appropriate level of reliability of forecasting and assess the security of mobile applications. The experimental results of our research are the evaluation of the weights of ten OWASP mobile application insecurity factors the identification of the mutual correlations of the consequences of mobile applications’ insecurity from these factors, and the identification of common factors on which more than one consequence depends.
Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

Summary This paper focuses on understanding how study participants interact and perceive a virtual crowd in an immersive virtual environment. Specifically, our within‐group exploratory study investigated how avoidance proximity variations (i.e., low, medium, and high avoidance proximity [defined as avoidance radius]) assigned to crowd agents impacted participants' interaction with the virtual crowd. During the study, we instructed our participants to walk in a virtual environment. At the same time, we had a virtual crowd scripted to walk toward the start position of the participant following a straight path. During the participants' walking task, we collected movement data (i.e., trajectory length and completion time) and immediately after each experimental condition, we asked participants to self‐report their experience (i.e., co‐presence, behavioral independence, crowd realism, crowd interaction realism, perceived politeness, and emotional reactivity). Based on the collected data, we found that when we exposed our participants to the high avoidance proximity condition, they: (1) followed longer paths, (2) spent more time reaching the target goal, (3) rated the virtual crowd less polite, (4) rated the virtual crowd and their interaction with the virtual crowd less realistic, (5) rated the behavior independence of the virtual crowd lower, and (6) self‐reported higher emotional reactivity. We discuss our findings and suggestions for further research on human‐virtual crowd interaction.
A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.