Digital transformation has become a critical strategic agenda across industrial security services to resolve recurring operational inefficiencies and visibility limitations. However, technology-based security solutions face persistent customer acceptance barriers within business-to-business emerging markets. This quantitative study aims to formulate an integrated marketing strategy framework for digital security services and identify priority initiatives supporting technology adoption. Data were gathered through structured questionnaires administered to 19 enterprise security decision-makers across industrial sectors in the Greater Jakarta region. Quantitative assessments utilized multi-criteria strategic evaluation models, including internal-external factor matrices and strategic priority matrices. The empirical findings reveal that data-based performance measurement difficulties and reporting delays represent the most critical customer pain points. Furthermore, quantitative priority testing established drone patrol system development as the paramount strategic initiative. The investigation concludes that implementing the networking, interaction, common interest, and experience framework with advanced artificial intelligence and internet-of-things ecosystems optimizes market penetration, strengthens corporate partnerships, and establishes sustainable competitive advantages in emerging markets. ABSTRAK Transformasi digital telah menjadi agenda strategis yang krusial di seluruh layanan keamanan industri untuk mengatasi inefisiensi operasional dan keterbatasan visibilitas yang berulang. Namun, solusi keamanan berbasis teknologi menghadapi hambatan penerimaan pelanggan yang persisten di pasar berkembang business-to-business (B2B). Studi kuantitatif ini bertujuan merumuskan kerangka strategi pemasaran terintegrasi untuk layanan keamanan digital serta mengidentifikasi inisiatif prioritas yang mendukung adopsi teknologi. Data dikumpulkan melalui kuesioner terstruktur yang dibagikan kepada 19 pengambil keputusan keamanan perusahaan di berbagai sektor industri di wilayah Jabodetabek (Greater Jakarta). Penilaian kuantitatif menggunakan model evaluasi strategis multikriteria, termasuk matriks faktor internal-eksternal (internal-external factor matrices) dan matriks prioritas strategis (strategic priority matrices). Temuan empiris mengungkapkan bahwa kesulitan pengukuran kinerja berbasis data dan keterlambatan pelaporan merupakan titik masalah pelanggan (pain points) yang paling kritis. Selain itu, pengujian prioritas kuantitatif menetapkan pengembangan sistem patroli drone (drone patrol system) sebagai inisiatif strategis utama. Investigasi ini menyimpulkan bahwa penerapan kerangka kerja networking, interaction, common interest, and experience (NICE) dengan ekosistem kecerdasan buatan (artificial intelligence) dan internet of things tingkat lanjut mampu mengoptimalkan penetrasi pasar, memperkuat kemitraan korporasi, serta membangun keunggulan kompetitif yang berkelanjutan di pasar berkembang (emerging markets).
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6