Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

“…With the popularity of intelligent chip in the application of edge terminal devices, a large number of AI applications will be deployed on the edge of networks closer to data sources in the future.The method based on DNN partition can realize deep learning model training and deployment on resource-constrained terminal devices, and solve the bottleneck problem of edge AI computing ability.Thekernel based partition method (KPM) was proposed as a new scheme on the basis of traditional workload based partition method (WPM).The quantitative analysis of inference performance was carried out from three aspects of computation FLOPS, memory consumption and communication cost respectively, and the qualitative analysis of the above two schemes was carried out from the perspective of flexibility, robustness and privacy of inference process.Finally, a software and hardware experimental platform was built, and AlexNet and VGG11 networks were implemented using PyTorch to further verify the performance advantages of the proposed scheme in terms of delay and energy consumption.It was concluded that, compared with the WPM scheme, the KPM scheme had better DNN reasoning acceleration effect in large-scale computing scenarios.And it has lower memory usage and energy consumption.…”
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Student attendance and evaluation system : A review by Sura Falih, Yaseen Ismael

“…Previous studies have explored a variety of approaches and techniques to achieve these goals, such as using RFID for automatic attendance recording, Arduino systems for automated testing, and machine learning algorithms for analyzing academic performance. …”
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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

“…With the popularity of intelligent chip in the application of edge terminal devices, a large number of AI applications will be deployed on the edge of networks closer to data sources in the future.The method based on DNN partition can realize deep learning model training and deployment on resource-constrained terminal devices, and solve the bottleneck problem of edge AI computing ability.Thekernel based partition method (KPM) was proposed as a new scheme on the basis of traditional workload based partition method (WPM).The quantitative analysis of inference performance was carried out from three aspects of computation FLOPS, memory consumption and communication cost respectively, and the qualitative analysis of the above two schemes was carried out from the perspective of flexibility, robustness and privacy of inference process.Finally, a software and hardware experimental platform was built, and AlexNet and VGG11 networks were implemented using PyTorch to further verify the performance advantages of the proposed scheme in terms of delay and energy consumption.It was concluded that, compared with the WPM scheme, the KPM scheme had better DNN reasoning acceleration effect in large-scale computing scenarios.And it has lower memory usage and energy consumption.…”
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The Impact of Artificial Intelligence on Human Resource Management in the Era of Digitalization by PhD. Mugur TOLICI1, PhD. Daniela NICULESCU

“…This study examines AI-driven HR functions, including recruitment, performance evaluation, and learning and development, while addressing ethical and operational challenges such as bias, data privacy, and job displacement. Using a qualitative methodology, including a narrative literature review and case study analysis, the research explores AI’s integration within HRM through theoretical frameworks such as the Technology Acceptance Model (TAM), Resource-Based View (RBV), and Socio-Technical Systems Theory. …”
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Computer-assisted diagnosis to improve diagnostic pathology: A review by Alessandro Caputo, Elisabetta Maffei, Nalini Gupta, Luca Cima, Francesco Merolla, Giorgio Cazzaniga, Pietro Pepe, Paolo Verze, Filippo Fraggetta

“…It emphasizes the strides made in digital pathology, the integration of AI, and the promising prospects for prognostic biomarker discovery using computational methods. Additionally, ethical considerations regarding data privacy, equity, and trust in AI deployment are examined. …”
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Quantifying participant distress: Validity and applicability of a distress measure to evaluate harm in quantitative assessments. by Jess MacArthur, Ratan Budhathoki, Min Prasad Basnet, Ambika Yadav, Sabitra Dhakal, Juliet Willetts

“…To support ethical considerations in quantitative survey deployment, we introduce a four-item formative measure to analyze interview ease, stress, privacy, and comprehension. We present the measure's conceptual and empirical development and examine the validity of the measure through data from Cambodia and Nepal (n = 4,674) using Partial Least Squares Structural Equation Modeling (PLS-SEM) for formative measurement model assessment. …”
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Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review by Abebe Belay Adege

“…Also, it reflects on how ethics in AI and ML for biomedical challenges address important issues such as bias, privacy, and accountability. It also underlines how different opportunities and challenges can be opened or addressed by the integration of AI-driven systems in biomedical workflows: engineering, clinicians, and data scientists have to cooperate. …”
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The Future of Artificial Intelligence in Interactive Learning: Trends, Challenges, Opportunities by Teguh Hidayat Iskandar Alam, Ika Safitri Windiarti

“…In addition, the study identifies the social, economic, and cultural impacts of AI adoption, and provides recommendations to mitigate challenges, such as data privacy and technology access. By providing new insights into future trends in AI in education, this study offers a basis for developing innovative and sustainable education policies and practices to meet global needs.…”
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Overreporting and Investigation in the New York City Child Welfare System: A Child’s Perspective by Daniella Rohr, Melissa Friedman

“…This piece argues that ACS’ investigative apparatus not only harms more children than it protects, but the tactics it employs violate the state and federal constitutional rights of children and their families. Using ACS’ own statistics, this piece demonstrates that New York unnecessarily investigates far too many, primarily Black and brown families; examines the harmful, and often unlawful reporting and investigation process in New York City; and enumerates reforms critical to protect both the safety and privacy rights of New York City’s children and families. …”
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Engaging Stakeholders in the Development of a National Digital Mental Health Strategy: Reflexive Thematic Analysis by Sarah Kennedy, Robyn Fitzgerald, Ruth Melia

“…Invited stakeholders included experts in DMH research, clinical practice, and mental health advocacy and policy, together with those with lived experience of accessing mental health services. Qualitative data were analyzed using a reflexive thematic analysis approach. …”
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The Impact of Artificial Intelligence (AI) on Students’ Academic Development by Aniella Mihaela Vieriu, Gabriel Petrea

“…Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. …”
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An efficient ECC and fuzzy verifier based user authentication protocol for IoT enabled WSNs by T. Sudhakar, R. Praveen, V. Natarajan

“…Ensuring integrity and privacy of data while transmitting it from sensors to the data analytics servers is crucial in open network. …”
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Efficient Elliptic-Curve-Cryptography-Based Anonymous Authentication for Internet of Things: Tailored Protocols for Periodic and Remote Control Traffic Patterns by Shunfang Hu, Yuanyuan Zhang, Yanru Guo, Yanru Chen, Liangyin Chen

“…IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. …”
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Transforming higher education with robotic process automation: enhancing efficiency, innovation, and student-centered learning by Vivek Bhardwaj, Mukesh Kumar

“…Inclusion criteria focused on studies published between 2020 and 2024 that addressed RPA applications in education. Data analysis was conducted using thematic synthesis to identify key trends across the selected studies.…”
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A Netnographic Analysis of ChatGPT Usage in Technology Forums in The Context of Society 5.0 by Serdar Pirtini, Songül Bilgili Sülük

“…However, users also expressed notable concerns about issues such as data security, privacy, and ethical considerations, underlining the dual nature of its societal impact. …”
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ChatGPT in orthodontics: limitations and possibilities by Lima Nara Grazieli Martins, Costa Lavínia, Santos Patrícia Bittencourt

“…ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. …”
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The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector by Rohail Akhtar Habib, Yumna Sattar Khan

“…The rising integration of AI in Pakistani medicine necessitates the evolution of regulatory frameworks to guarantee patient safety, data privacy, and ethical application of AI technologies. …”
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Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications by Mohammad Kamrul Hasan, Shayla Islam, Rossilawati Sulaiman, Sheroz Khan, Aisha-Hassan Abdalla Hashim, Shabana Habib, Mohammad Islam, Saleh Alyahya, Musse Mohamed Ahmed, Samar Kamil, Md Arif Hassan

“…Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. …”
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The Application of Artificial Intelligence in Medical Diagnostics: Implications for Sports Medicine by Michał Bolek, Dominika Musialska, Aleksandra Kędzia, Bartosz Jagieła, Monika Fidyk, Magda Minkiewicz, Maciej Dyda

“…However, their integration into healthcare raises critical ethical concerns related to data privacy, algorithmic bias, and transparency. …”
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FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning by Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu, Huijuan Lu

“…Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. …”
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