Yulin Yao, Kangfeng Zheng, Bin Wu^*, Chunhua Wu, Jiaqi Gao, Jvjie Wang, Minjiao Yang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4753-4776, 2025, DOI:10.32604/cmc.2025.065059 - 23 October 2025

Abstract Cognitive biases are commonly used by attackers to manipulate users’ psychology in phishing emails. This study systematically analyzes the exploitation of cognitive biases in phishing emails and addresses the following questions: (1) Which cognitive biases are frequently exploited in phishing emails? (2) How are cognitive biases exploited in phishing emails? (3) How effective are cognitive bias features in detecting phishing emails? (4) How can the exploitation of cognitive biases in phishing emails be modelled? To address these questions, this study constructed a cognitive processing model that explains how attackers manipulate users by leveraging cognitive biases More >

Shunying Zhao^1,2,*, Baojuan Ye^2,*, Min Rao², Yulan Guo¹

Journal of Psychology in Africa, Vol.35, No.4, pp. 463-470, 2025, DOI:10.32604/jpa.2025.070060 - 17 August 2025

Abstract This study investigated how components of threat-related attentional biases are associated with levels of sense of control. Utilizing a using a spatial-cueing paradigm, 36 college students with a high sense of control (females = 22, M_age = 19.44, SD = 1.36) and 35 with a low sense of control (females = 15, M_age = 19.77, SD = 1.40) were assigned to task featuring different cue-target intervals (i.e., 50 and 800 ms). The student participants completed the Control Sense Scale, the GAD-7 Anxiety Scale, and the PHQ-9 Patient Health Questionnaire. Data from employing spatial-cueing task procedure, would provide… More >

Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma

Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >

Lihua Zuo^1,2,#, Jian Mao^2,#,*

Journal of Psychology in Africa, Vol.35, No.2, pp. 241-247, 2025, DOI:10.32604/jpa.2025.065765 - 30 June 2025

Abstract This study examined how social desirability responses confound the relationship between gender and social media addiction. A total of 496 college student social media users (females = 310, 62.5%, mean age = 20.15, SD = 1.26) completed an online questionnaire on Social Media Addiction and Social Desirability. Mediation analysis revealed that females were at higher risk for social media addiction. On the other hand, the indirect effect of gender on social media addiction via social desirability is associated with lower social media addiction, which suggests that social desirability had a suppression effect on social media More >

Zichen Li^1,#, Jukui Ma^2,#, Minjun Liu³, Guowei Geng^1,*, Hongxia Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1841-1856, 2025, DOI:10.32604/phyton.2025.066148 - 27 June 2025

Abstract Sweet potatoes are significant cash crops, however, their yield and quality are greatly compromised by viral diseases. In this study, the complete genomic sequences of two Sweet Potato Virus 2 (SPV2) isolates from infected sweet potato leaves in the Shandong (designated as SPV2-SDYT, GenBank No. PQ855660.1) and Jiangsu (designated as SPV2-JSXZ, GenBank No. PQ855661.1) provinces in China were obtained using 5^′ RACE and RT-PCR amplification. Consistency, phylogeny, codon usage bias, recombination, and selection pressure analyses were conducted using the SPV2-SDYT and SPV2-JSXZ genome sequences. The complete genome sequences of SPV2-SDYT and SPV2-JSXZ were 10561 nucleotides (nt)… More >

Jiahao Wu^1,2, Jinzhong Xu¹, Xiaoming Liu^1,*, Guan Yang^1,3, Jie Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2809-2828, 2025, DOI:10.32604/cmc.2025.061359 - 16 April 2025

Abstract Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain, due to the entity bias arising from the variation of entity information between different domains, which makes large language models prone to spurious correlations problems when dealing with specific domains and entities. In order to solve this problem, this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement, which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing… More >

Jianfeng Lu¹, Tao Huang¹, Yuanai Xie^2,*, Shuqin Cao¹, Bing Li³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 619-634, 2025, DOI:10.32604/cmc.2025.060094 - 26 March 2025

Abstract The proliferation of deep learning (DL) has amplified the demand for processing large and complex datasets for tasks such as modeling, classification, and identification. However, traditional DL methods compromise client privacy by collecting sensitive data, underscoring the necessity for privacy-preserving solutions like Federated Learning (FL). FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data. Given that FL clients autonomously manage training data, encouraging client engagement is pivotal for successful model training. To overcome challenges like unreliable communication and budget constraints, we present ENTIRE, a contract-based dynamic… More >

Yuchen Ye^1,3, Dadi Wu², Jiahu Hao^1,2,*

International Journal of Mental Health Promotion, Vol.26, No.12, pp. 1009-1016, 2024, DOI:10.32604/ijmhp.2024.056235 - 31 December 2024

Abstract Objective: In recent years, psychological problems in pregnant women have become an important public health problem. Depression is a common psychological problem during pregnancy. At present, most studies focus on prenatal depression in pregnant women, and there is a lack of relevant studies on prenatal negative cognition and its relationship with depression. This study aims to examine the relationship between depression and negative cognitive bias in women in late pregnancy and identify the influencing factors. Methods: A total of 829 women in late pregnancy were recruited from a tertiary hospital between April 2023 and October… More >

Wu-Jiu Sun¹, Xiao Fan Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4551-4573, 2024, DOI:10.32604/cmc.2024.057714 - 19 December 2024

Abstract Microblogging platforms like X (formerly Twitter) and Sina Weibo have become key channels for spreading information online. Accurately predicting information spread, such as users’ reposting activities, is essential for applications including content recommendation and analyzing public sentiment. Current advanced models rely on deep representation learning to extract features from various inputs, such as users’ social connections and repost history, to forecast reposting behavior. Nonetheless, these models frequently ignore intrinsic confounding factors, which may cause the models to capture spurious relationships, ultimately impacting prediction performance. To address this limitation, we propose a novel Debiased Reposting Prediction… More >

Qi Wang¹, Chenxin Li^1,*, Chichen Lin², Weijian Fan³, Shuang Feng¹, Yuanzhong Wang⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3351-3369, 2024, DOI:10.32604/cmc.2024.057191 - 18 November 2024

Abstract News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem. Most previous works only extract features and evaluate media from one dimension independently, ignoring the interconnections between different aspects. This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features. This framework models the relationship and interaction between media bias and factuality, utilizing this relationship to assist in the prediction of profiling results. Our approach extracts features independently while aligning and fusing them through recursive convolution and More >