Being Reformed today? by D.J. Smit

“…It engages with important historical figures and documents (Calvin, Barth, Barmen) as well as more contemporary works and statements (Wolterstorff, Niebuhr, Leith, Gerrish, Plasger, Welker, Nimmo and Fergusson, Boesak, Botman, Belhar, Kitwe, Debrecen, Accra) on being Reformed. …”
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Oor die inhoud en boodskap van die Heidelbergse Kategismus by D. Smit

“…A third section serves as reminder of Barth’s claim that Jesus Christ is the content of this comfort and therefore of the Heidelberg Catechism. …”
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KAJIAN STRUKTUR EKONOMI KABUPATEN BEKASI by YUKHA SUNDAYA, Ina Helena Agustina

“…Kabupaten Bekasi merupakan salah satu daerah dengan Pendapatan asli Daerah terbesar di Jawa Barat,maka kajian struktur ekonomi sangat penting untuk menunjang kinerja pembangunan. …”
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MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network by Wenjie Guo, Wenbiao Du, Xiuqi Yang, Jingfeng Xue, Yong Wang, Weijie Han, Jingjing Hu

“…Firstly, to ensure semantic richness, a Bidirectional Encoder Representations from Transformers-based (BERT) attribute-enhanced function embedding method is proposed for the extraction of node attributes in the function call graph. …”
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Cross-Community Question Relevance Prediction for Stack Overflow and GitHub by Song Yu, Bugao Jiang, Danni Zhang, Zhifang Liao

“…Compared to the latest models (MQDD, CodeBERT, ASIM), CCQRP demonstrates an improvement in F1-score ranging from 0.60% to 10.86% and exhibits robust generalization capabilities.…”
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Sentiment works in small-cap stocks: Japanese stock’s sentiment with language models by Masahiro Suzuki, Yasushi Ishikawa, Masayuki Teraguchi, Hiroki Sakaji

“…Comparisons between the models showed higher returns at high sentiment for the model trained with the existing sentiment dataset and lower returns at low sentiment for ChatGPT. The DeBERTaV2 model trained on Economy Watchers Survey data performed best in terms of returns at the highest sentiment quantile.…”
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Exploring Food Safety Emergency Incidents on Sina Weibo: Using Text Mining and Sentiment Evolution by Biao Ma, Ruihan Zheng

“…Subsequently, the study employed an innovative approach by combining BERT-TextCNN and BERTopic models for a thorough analysis of sentiment and thematic aspects of the textual data. …”
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Are Women More Risk Averse? A Sequel by Christos I. Giannikos, Efstathia D. Korkou

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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models by Weichuan Mo, Kongyang Chen, Yatie Xiao

“…Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). …”
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Insider threat detection based on operational attention and data augmentation by Guanyun FENG, Cai FU, Jianqiang LYU, Lansheng HAN

“…In recent years, there has been an increased focus on the issue of insider threats.Insider threats are a major cause security breaches in organizations and pose an ongoing challenge.By analyzing the existing insider threat data, it was identified that the biggest challenge in insider threat detection lies in data imbalance and the limited number of labeled threat samples.In the Cert R4.2 dataset, which is a classic dataset for insider threat detection, there are over 3.22 million log data, but only 7,423 are marked as malicious operation logs.Furthermore, most of the operation types in the logs are not related to malicious behavior, and only two types of operations are highly correlated with malicious behavior, such as leaking company data, creating interference in the detection process.To address this challenge, a data processing framework was designed based on operational attention and data augmentation.Anomaly evaluation was first performed on operations by the framework, and operations with low anomaly scores were then masked.This makes the model better focus on operations related to malicious behavior, which can be considered as a hard attention mechanism for operations.Next, the characteristics of the insider threat dataset were analyzed, and three rules were designed for data augmentation on malicious samples to increase the diversity of samples and alleviate the substantial imbalance between positive and negative samples.Supervised insider threat detection was regarded as a time-series classification problem.Residual connections were added to the LSTM-FCN model to achieve multi-granularity detection, and indicators such as precision rate and recall rate were used to evaluate the model.The results indicate superior performance over existing baseline models.Moreover, the data processing framework was implemented on various classic models, such as ITD-Bert and TextCNN, and the results show that the methods effectively improve the performance of insider threat detection models.…”
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Hate Speech Detection Using Large Language Models: A Comprehensive Review by Aish Albladi, Minarul Islam, Amit Das, Maryam Bigonah, Zheng Zhang, Fatemeh Jamshidi, Mostafa Rahgouy, Nilanjana Raychawdhary, Daniela Marghitu, Cheryl Seals

“…This paper provides a comprehensive review of the application of large language models (LLMs) like GPT-3, BERT, and their successors in hate speech detection. …”
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Profil Kasus Tuberkulosis Paru di Instalasi Rawat Inap Paru RSUP Dr. M. Djamil Padang Periode 1 Januari 2010 - 31 Desember 2011 by Muhammad Gamal Eddin, Oea Khairsyaf, Elly Usman

“…Laki - laki (72%), usia 20- 29 tahun (27%), pendidikan tamat sekolah lanjut tingkat atas (SLTA)(47%), pekerjaan rumah tangga (33%) merupakan karakteristik terbanyak diikuti merokok pada laki- laki (64%) dan status gizi kurus dengan kekurangan berat badan tingkat berat (53%). Hasil data analisis berdasarkan Chi Square, didapatkan X 2<br />= 2,5 dengan α= 0,05, sehingga tidak ada hubungan bermakna antara jenis kelamin dengan hasil pemeriksaan BTA sputum. …”
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Penerapan Model Enterprise Architecture Cube Pada Perencanaan Strategi Sistem Dan Teknologi Informasi by Delia Apriana, Sawali Wahyu

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Growth of Zinc Oxide Nanorods with the Thickness of Less than or Equal to 1 μm through Zinc Acetate or Zinc Nitrate for Perovskite Solar Cell Applications by Albertus Bramantyo, Kenji Murakami, Masayuki Okuya, Arief Udhiarto, Nji Raden Poespawati

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FRACTURE PROPERTIES OF PIANO STRINGS ON THE V-BAR by HU QianRu, ZHUAN Xin

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Diabetic Foot Care Plan by Gordon Dow, The Diabetic Foot Care Plan Working Group

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JOHN WANSBROUGH: STUDI ATAS TRADISI DAN INSTRUMEN TAFSIR ALQUR’AN KLASIK by Syamsul Wathani

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Assessment of Selected Proximate and Heavy Metals in Enset Products Collected from Tepi Market, Southwestern Ethiopia by Shisho Haile Geleta

“…This study focused on determining selected proximate compositions, such as moisture content, ash content, crude fibre content, and crude fat content, of kocho and bulla enset products collected from Bechi, Fide, Yeki, Zinki, Addis Alem, and Korcha kebeles of Yeki woreda. …”
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Réflexions critiques autour des frontières de la péninsule Ibérique au premier âge du Fer by Vanessa Rodrigues

“…The traditional approach has been to map some remains in order to recognize cultural areas that may coincide with the ethnic territories described by greco-latin literature. F. Barth denounced the approach of ethnic categories based on their cultural content to favour study of their boundaries. …”
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