Journal articles: 'Personalized summarization'

1

Díaz, Alberto, and Pablo Gervás. "User-model based personalized summarization." Information Processing & Management 43, no. 6 (November 2007): 1715–34. http://dx.doi.org/10.1016/j.ipm.2007.01.009.

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Chen, Fan, Christophe De Vleeschouwer, and Andrea Cavallaro. "Resource Allocation for Personalized Video Summarization." IEEE Transactions on Multimedia 16, no. 2 (February 2014): 455–69. http://dx.doi.org/10.1109/tmm.2013.2291967.

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Li, Junjie, Haoran Li, and Chengqing Zong. "Towards Personalized Review Summarization via User-Aware Sequence Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6690–97. http://dx.doi.org/10.1609/aaai.v33i01.33016690.

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We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.

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WU, XINDONG, FEI XIE, GONGQING WU, and WEI DING. "PNFS: PERSONALIZED WEB NEWS FILTERING AND SUMMARIZATION." International Journal on Artificial Intelligence Tools 22, no. 05 (October 2013): 1360007. http://dx.doi.org/10.1142/s0218213013600075.

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Information on the World Wide Web is congested with large amounts of news contents. Recommending, filtering, and summarization of Web news have become hot topics of research in Web intelligence, aiming to find interesting news for users and give concise content for reading. This paper presents our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. Two Web news recommendation methods are proposed to keep tracking news and find topic interesting news for users. A keyword knowledge base is maintained and provides real-time updates to reflect the news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. A keyword extraction method based on lexical chains is proposed that uses the semantic similarity and the relatedness degree to represent the semantic relations between words. Word sense disambiguation is also performed in the built lexical chains. Experiments on Web news pages and journal articles show that the proposed keyword extraction method is effective. An example run of our PNFS system demonstrates the superiority of this Web intelligence system.

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Ul Haq, Ijaz, Amin Ullah, Khan Muhammad, Mi Young Lee, and Sung Wook Baik. "Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition." Complexity 2019 (May 5, 2019): 1–10. http://dx.doi.org/10.1155/2019/3581419.

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Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods.

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Zhang, Yujia, Michael Kampffmeyer, Xiaoguang Zhao, and Min Tan. "Deep Reinforcement Learning for Query-Conditioned Video Summarization." Applied Sciences 9, no. 4 (February 21, 2019): 750. http://dx.doi.org/10.3390/app9040750.

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Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given query. Thus it can be tailored to different user interests leading to a better personalized summary and differs from the generic video summarization which only focuses on video content. Our work targets this query-conditioned video summarization task, by first proposing a Mapping Network (MapNet) in order to express how related a shot is to a given query. MapNet helps establish the relation between the two different modalities (videos and query), which allows mapping of visual information to query space. After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. These rewards jointly guide the agent to select the most representative and diversity video shots that are most related to the user query. Experimental results on a query-conditioned video summarization benchmark demonstrate the effectiveness of our proposed method, indicating the usefulness of the proposed mapping mechanism as well as the reinforcement learning approach.

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CHEN, Sinan, and Masahide NAKAMURA. "Generating Personalized Dialogues Based on Conversation Log Summarization." Proceedings of Design & Systems Conference 2021.31 (2021): 3407. http://dx.doi.org/10.1299/jsmedsd.2021.31.3407.

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Li, Jianxin, Chengfei Liu, Jeffrey Xu Yu, Yi Chen, Timos Sellis, and J. Shane Culpepper. "Personalized Influential Topic Search via Social Network Summarization." IEEE Transactions on Knowledge and Data Engineering 28, no. 7 (July 1, 2016): 1820–34. http://dx.doi.org/10.1109/tkde.2016.2542804.

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Liang, Chao, Changsheng Xu, and Hanqing Lu. "Personalized Sports Video Customization Using Content and Context Analysis." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/836357.

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We present an integrated framework on personalized sports video customization, which addresses three research issues: semantic video annotation, personalized video retrieval and summarization, and system adaptation. Sports video annotation serves as the foundation of the video customization system. To acquire detailed description of video content, external web text is adopted to align with the related sports video according to their semantic correspondence. Based on the derived semantic annotation, a user-participant multiconstraint 0/1 Knapsack model is designed to model the personalized video customization, which can unify both video retrieval and summarization with different fusion parameters. As a measure to make the system adaptive to the particular user, a social network based system adaptation algorithm is proposed to learn latent user preference implicitly. Both quantitative and qualitative experiments conducted on twelve broadcast basketball and football videos validate the effectiveness of the proposed method.

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Chen, Yu-Hsiu, Pin-Yu Chen, Hong-Han Shuai, and Wen-Chih Peng. "TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7538–45. http://dx.doi.org/10.1609/aaai.v34i05.6252.

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We address personalized Electronic Direct Mail (EDM) subject generation, which generates an attractive subject line for a product description according to user's preference on different contents or writing styles. Generating personalized EDM subjects has a few notable differences from generating text summaries. The subject has to be not only faithful to the description itself but also attractive to increase the click-through rate. Moreover, different users may have different preferences over the styles of topics. We propose a novel personalized EDM subject generation model named Soft Template-based Personalized EDM Subject Generator (TemPEST) to consider the aforementioned users' characteristics when generating subjects, which contains a soft template-based selective encoder network, a user rating encoder network, a summary decoder network and a rating decoder. Experimental results indicate that TemPEST is able to generate personalized topics and also effectively perform recommending rating reconstruction.

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Xu, Hongyan, Hongtao Liu, Wang Zhang, Pengfei Jiao, and Wenjun Wang. "Rating-boosted abstractive review summarization with neural personalized generation." Knowledge-Based Systems 218 (April 2021): 106858. http://dx.doi.org/10.1016/j.knosys.2021.106858.

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Hamza, Rafik, Khan Muhammad, Zhihan Lv, and Faiza Titouna. "Secure video summarization framework for personalized wireless capsule endoscopy." Pervasive and Mobile Computing 41 (October 2017): 436–50. http://dx.doi.org/10.1016/j.pmcj.2017.03.011.

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S, Sai Shashank, Sindhu S, Vineeth V, and Pranathi C. "VIDEO SUMMARIZATION." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 277–80. http://dx.doi.org/10.26562/irjcs.2022.v0908.24.

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The general public now has access to a vast amount of multimedia information thanks to recent technological advancements and the quick expansion of consumer electronics, making it challenging to effectively consume video material among the thousands of options accessible. By choosing and presenting the most educational or fascinating materials for users, we provide a method to quickly summarize the content of a lengthy video document. The practice of condensing a raw video into a more manageable form without losing much information is known as video summarizing. Either a comprehensive analysis of the full movie or the local differences between neighboring frames are used to achieve this. The majority of such approaches rely on universal characteristics like color, texture, motion data, etc. Video summaries are evaluated depending on the sort of content they are formed from (object, event, perception, or feature-based) and the functionality made available to the user for consumption (interactive or static, personalized or generic). The suggested system analyses each frame of a video as input before producing a summary. Each frame receives a score that is used to compare it to a threshold value in the final phase. Every frame whose frame score exceeds the threshold is chosen as a key frame and is represented in the final movie summary. This technique enables us to condense video information of various lengths while guaranteeing that the key moments are included. The purpose of video summary is to facilitate quick access, speed up browsing through a sizable video database, and offer a condensed video representation while maintaining the core activities of the original video.

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Kannan, Rajkumar, Sridhar Swaminathan, Gheorghita Ghinea, Frederic Andres, and Kalaiarasi Sonai Muthu Anbananthen. "Movie Video Summarization- Generating Personalized Summaries Using Spatiotemporal Salient Region Detection." International Journal of Multimedia Data Engineering and Management 10, no. 3 (July 2019): 1–26. http://dx.doi.org/10.4018/ijmdem.2019070101.

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Video summarization condenses a video by extracting its informative and interesting segments. In this article, a novel video summarization approach is proposed based on spatiotemporal salient region detection. The proposed approach first segments a video into a set of shots which are ranked with spatiotemporal saliency scores. The score for a shot is computed by aggregating the frame level spatiotemporal saliency scores. This approach detects spatial and temporal salient regions separately using different saliency theories related to objects present in a visual scenario. The spatial saliency of a video frame is computed using color contrast and color distribution estimations and center prior integration. The temporal saliency of a video frame is estimated as an integration of local and global temporal saliencies computed using patch level optical flow abstractions. Finally, top ranked shots with the highest saliency scores are selected for generating the video summary. The objective and subjective experimental results demonstrate the efficacy of the proposed approach.

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Park, Sun, ByungRae Cha, and JangWoo Kwon. "Personalized Document Summarization Using Pseudo Relevance Feedback and Semantic Feature." IETE Journal of Research 58, no. 2 (2012): 155. http://dx.doi.org/10.4103/0377-2063.96182.

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Varini, Patrizia, Giuseppe Serra, and Rita Cucchiara. "Personalized Egocentric Video Summarization of Cultural Tour on User Preferences Input." IEEE Transactions on Multimedia 19, no. 12 (December 2017): 2832–45. http://dx.doi.org/10.1109/tmm.2017.2705915.

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Yin, Yifang, Roshan Thapliya, and Roger Zimmermann. "Encoded Semantic Tree for Automatic User Profiling Applied to Personalized Video Summarization." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 1 (January 2018): 181–92. http://dx.doi.org/10.1109/tcsvt.2016.2602832.

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Wang, Zhehao. "English News Text Recommendation Method Based on Hypergraph Random Walk Label Expansion." Computational Intelligence and Neuroscience 2022 (March 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/1880114.

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With the rapid development of multimedia and Internet technology, English news text summary technology has received widespread attention as a way to quickly obtain English news text content. The existing method of English news text summarization based on graph model usually takes English news text as the vertices of the graph, and the relationship between the two vertices is represented by the edge. Although it has achieved good results, it cannot quickly sort out the English Complex relationships between news texts. In order to solve this problem, this paper uses the hypergraph model to model the relationship between English news text, and conducts in-depth research on the application of the hypergraph model in the field of English news text summarization. The high popularity of the Internet has brought about earth-shaking changes to the news industry, which makes the news on the Internet a great way for netizens to get news. However, the public cannot pick out satisfactory events from a large amount of news. In order to solve this problem, news event discovery technology that can help users quickly discover and understand hot news is produced. In addition, user personalized recommendation technology will rely on customer operation habits to provide customers with hot events of interest. The personalized news recommendation method adopted in this article has the advantages of integrating discovery and personalized news recommendation, and provides users with a better experience. Compared with the traditional hierarchical clustering algorithm, the algorithm proposed in this paper significantly improves the accuracy.

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Grani, Giorgio, Andrea Lenzi, and Paola Velardi. "Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism." ACM Transactions on Computing for Healthcare 3, no. 1 (January 31, 2022): 1–28. http://dx.doi.org/10.1145/3468781.

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Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization ( GTCACS ), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions ( ADRs ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.

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Bouras, Christos, and Vassilis Tsogkas. "Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop." Data & Knowledge Engineering 69, no. 7 (July 2010): 664–77. http://dx.doi.org/10.1016/j.datak.2010.02.005.

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Guo, Guibing, Bowei Chen, Xiaoyan Zhang, Zhirong Liu, Zhenhua Dong, and Xiuqiang He. "Leveraging Title-Abstract Attentive Semantics for Paper Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 67–74. http://dx.doi.org/10.1609/aaai.v34i01.5335.

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Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

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Park, Han-Saem, and Sung-Bae Cho. "A personalized summarization of video life-logs from an indoor multi-camera system using a fuzzy rule-based system with domain knowledge." Information Systems 36, no. 8 (December 2011): 1124–34. http://dx.doi.org/10.1016/j.is.2011.04.005.

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Zhang, Yu, Jiayu Li, and Xiaoping Jiang. "Household Physical Activity for Adults in the Context of the Pandemic: A Systematic Review." Sustainability 14, no. 22 (November 17, 2022): 15257. http://dx.doi.org/10.3390/su142215257.

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Background: People were isolated at home during the COVID-19 pandemic and were restricted from going outside, leaving them with the option of physical activity at home. The purpose of this paper is to examine how home isolation during an epidemic changes adult lifestyle and health behaviors and the role of physical activity during home isolation in improving adult dysphoria. Methods: Four major databases were searched and the 21 final included papers on home physical activity during the epidemic were evaluated. The literature was analyzed and evaluated using generalization, summarization, analysis, and evaluation methods. The findings revealed that home isolation during the epidemic changed the lifestyle and physical activity behavior of adults. Participation in physical activity varied among different levels of the population during home isolation for the epidemic. In addition, physical activity in home isolation during the epidemic helped improve adults’ poor mood. The negative impact of prolonged home isolation on the health of the global population cannot be ignored, and more encouragement should be given to diversified indoor physical activities to maintain physical and mental health. In addition, there is a need to develop more personalized technology tools for physical activity supervision regarding use.

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Kosvyra, Alexandra, C. Maramis, and I. Chouvarda. "Developing an Integrated Genomic Profile for Cancer Patients with the Use of NGS Data." Emerging Science Journal 3, no. 3 (June 3, 2019): 157–67. http://dx.doi.org/10.28991/esj-2019-01178.

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Next Generation Sequencing (NGS) technologies has revolutionized genomics data research by facilitating high-throughput sequencing of genetic material that comes from different sources, such as Whole Exome Sequencing (WES) and RNA Sequencing (RNAseq). The exploitation and integration of this wealth of heterogeneous sequencing data remains a major challenge. There is a clear need for approaches that attempt to process and combine the aforementioned sources in order to create an integrated profile of a patient that will allow us to build the complete picture of a disease. This work introduces such an integrated profile using Chronic Lymphocytic Leukemia (CLL) as the exemplary cancer type. The approach described in this paper links the various NGS sources with the patients’ clinical data. The resulting profile efficiently summarizes the large-scale datasets, links the results with the clinical profile of the patient and correlates indicators arising from different data types. With the use of state-of-the-art machine learning techniques and the association of the clinical information with these indicators, which served as the feature pool for the classification, it has been possible to build efficient predictive models. To ensure reproducibility of the results, open data were exclusively used in the classification assessment. The final goal is to design a complete genomic profile of a cancer patient. The profile includes summarization and visualization of the results of WES and RNAseq analysis (specific variants and significantly expressed genes, respectively) and the clinical profile, integration/comparison of these results and a prediction regarding the disease trajectory. Concluding, this work has managed to produce a comprehensive clinico-genetic profile of a patient by successfully integrating heterogeneous data sources. The proposed profile can contribute to the medical research providing new possibilities in personalized medicine and prognostic views.

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Li, Dan, Tong Xu, Peilun Zhou, Weidong He, Yanbin Hao, Yi Zheng, and Enhong Chen. "Social Context-aware Person Search in Videos via Multi-modal Cues." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–25. http://dx.doi.org/10.1145/3480967.

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Person search has long been treated as a crucial and challenging task to support deeper insight in personalized summarization and personality discovery. Traditional methods, e.g., person re-identification and face recognition techniques, which profile video characters based on visual information, are often limited by relatively fixed poses or small variation of viewpoints and suffer from more realistic scenes with high motion complexity (e.g., movies). At the same time, long videos such as movies often have logical story lines and are composed of continuously developmental plots. In this situation, different persons usually meet on a specific occasion, in which informative social cues are performed. We notice that these social cues could semantically profile their personality and benefit person search task in two aspects. First, persons with certain relationships usually co-occur in short intervals; in case one of them is easier to be identified, the social relation cues extracted from their co-occurrences could further benefit the identification for the harder ones. Second, social relations could reveal the association between certain scenes and characters (e.g., classmate relationship may only exist among students), which could narrow down candidates into certain persons with a specific relationship. In this way, high-level social relation cues could improve the effectiveness of person search. Along this line, in this article, we propose a social context-aware framework, which fuses visual and social contexts to profile persons in more semantic perspectives and better deal with person search task in complex scenarios. Specifically, we first segment videos into several independent scene units and abstract out social contexts within these scene units. Then, we construct inner-personal links through a graph formulation operation for each scene unit, in which both visual cues and relation cues are considered. Finally, we perform a relation-aware label propagation to identify characters’ occurrences, combining low-level semantic cues (i.e., visual cues) and high-level semantic cues (i.e., relation cues) to further enhance the accuracy. Experiments on real-world datasets validate that our solution outperforms several competitive baselines.

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Kuppusamy, KS, and G. Aghila. "Segmentation Based Personalized Web Page Summarization Model." Journal of Advances in Information Technology 3, no. 3 (August 1, 2012). http://dx.doi.org/10.4304/jait.3.3.155-161.

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T., Salah, Khaled M., and Naveed Arshad. "Personalized Semantic Retrieval and Summarization of Web Based Documents." International Journal of Advanced Computer Science and Applications 4, no. 1 (2013). http://dx.doi.org/10.14569/ijacsa.2013.040128.

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Garmastewira, Garmastewira, and Masayu Leylia Khodra. "SUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK." Journal of Information and Communication Technology, June 10, 2019. http://dx.doi.org/10.32890/jict2019.18.3.4675.

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Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.

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Mujtaba, Ghulam, Adeel Malik, and Eun-Seok Ryu. "LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3209275.

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Gao, Jing, Xue Shen, Randy Ko, Cong Huang, and Changbing Shen. "Cognitive Process of Psoriasis and Its Comorbidities: From Epidemiology to Genetics." Frontiers in Genetics 12 (November 26, 2021). http://dx.doi.org/10.3389/fgene.2021.735124.

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Psoriasis (PsO) is a chronic inflammatory skin disease that affects approximately 2% of the population all over the world. Comorbidities of PsO have increasingly garnered more interest in the past decades. Compared with the normal population, the incidences of comorbidities are higher among patients with PsO. In the last 20 years, researchers have focused on studying the genetic components of PsO, and genetic associations between PsO and its comorbidities were elucidated. This review provides an in-depth understanding and summarization of the connection between PsO and its comorbidities from the perspectives of epidemiology and genetics. Further understanding of PsO and its comorbidities will promote research on the pathogenesis, drug development, novel therapy methods, and personalized and precision treatment of PsO and its comorbidities.

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Peng, Tao, Yangyang Sun, Zhiwei Lv, Ze Zhang, Quanxin Su, Hao Wu, Wei Zhang, et al. "Effects of FGFR4 G388R, V10I polymorphisms on the likelihood of cancer." Scientific Reports 11, no. 1 (January 14, 2021). http://dx.doi.org/10.1038/s41598-020-80146-y.

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AbstractThe correlation between G388R or V10I polymorphisms of fibroblast growth factor receptor (FGFR) 4 gene and the risk of carcinoma has been investigated previously, but the results are contradictory. Odds ratios (ORs) with 95% confidence intervals (95%CIs), in silico tools, and immunohistochemical staining (IHS) were adopted to assess the association. In total, 13,793 cancer patients and 16,179 controls were evaluated in our pooled analysis. Summarization of all the studies showed that G388R polymorphism is associated with elevated susceptibility to cancer under homozygous comparison (OR = 1.21, 95%CI = 1.03–1.43, P = 0.020) and a recessive genetic model (OR = 1.21, 95%CI = 1.04–1.41, P = 0.012). In the stratification analysis by cancer type and ethnicity, similar findings were indicated for prostate cancer, breast cancer, and individuals of Asian descendant. Polyphen2 bioinformatics analysis showed that the G388R mutation is predicted to damage the protein function of FGFR4. IHS analysis indicated that FGFR4 expression is increased in advanced prostate cancer. These findings may guide personalized treatment of certain types of cancers. Up-regulation of FGFR4 may be related to a poor prognosis in prostate cancer.

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Conijn, Rianne, Christine Cook, Menno van Zaanen, and Luuk Van Waes. "Early prediction of writing quality using keystroke logging." International Journal of Artificial Intelligence in Education, August 24, 2021. http://dx.doi.org/10.1007/s40593-021-00268-w.

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AbstractFeedback is important to improve writing quality; however, to provide timely and personalized feedback is a time-intensive task. Currently, most literature focuses on providing (human or machine) support on product characteristics, especially after a draft is submitted. However, this does not assist students who struggle during the writing process. Therefore, in this study, we investigate the use of keystroke analysis to predict writing quality throughout the writing process. Keystroke data were analyzed from 126 English as a second language learners performing a timed academic summarization task. Writing quality was measured using participants’ final grade. Based on previous literature, 54 keystroke features were extracted. Correlational analyses were conducted to identify the relationship between keystroke features and writing quality. Next, machine learning models (regression and classification) were used to predict final grade and classify students who might need support at several points during the writing process. The results show that, in contrast to previous work, the relationship between writing quality and keystroke data was rather limited. None of the regression models outperformed the baseline, and the classification models were only slightly better than the majority class baseline (highest AUC = 0.57). In addition, the relationship between keystroke features and writing quality changed throughout the course of the writing process. To conclude, the relationship between keystroke data and writing quality might be less clear than previously posited.

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Journal articles on the topic 'Personalized summarization'

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