作者: 愛了嗎 時(shí)間: 2025-3-21 20:41 作者: 體貼 時(shí)間: 2025-3-22 04:05
Richard Beals,Roderick S. C. Wongrency and trust can be ensured in the underlying distributions and relationships of the resulting synthetic datasets. What is more, these datasets offer a strong level of privacy through lower risks of identifying real patients.作者: Scleroderma 時(shí)間: 2025-3-22 05:03
Emergent Imputative Symbols: In One Worde staging information). In this paper, we explore the combination of pseudo time and topological data analysis to build realistic trajectories over disease topologies. Using three different datasets: simulated, diabetes and genomic data, we explore how the combined method can highlight distinct temp作者: 江湖騙子 時(shí)間: 2025-3-22 09:29 作者: 謙虛的人 時(shí)間: 2025-3-22 16:03
https://doi.org/10.1007/978-981-15-1963-5r processors typically only have integer processing capabilities. This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power processors, is privacy-preserving, and effectively minimizes communication. The empiri作者: 謙虛的人 時(shí)間: 2025-3-22 18:15
https://doi.org/10.1007/978-981-10-6811-9us data batches. We then applied a classification method based on Text-CNN technique to classify packets as normal or attack inside each suspicious batch. Our model reconstruction results show that we are able to discriminate normal and attack models with high precision and our classification method作者: SPURN 時(shí)間: 2025-3-22 21:29
y unsupervised, our method outperforms many existing approaches. To the best of our knowledge, the only approaches with comparable performance require manual filtering of connections and feature engineering steps. In contrast, our method is applied to raw network traffic. We apply our pipeline to th作者: 討好女人 時(shí)間: 2025-3-23 01:30
Alan Park FRICS, MCIOB, ACIArb., FAFMs using call graph, n-gram, and image transformations. Further, Auxiliary Classifier Generative Adversarial Network (AC-GAN) is used for generating unseen data for training purposes. The model is extended for federated setup to build an effective malware detection system. We have used the Microsoft 作者: lesion 時(shí)間: 2025-3-23 09:23 作者: 追蹤 時(shí)間: 2025-3-23 10:44 作者: 圓桶 時(shí)間: 2025-3-23 13:55
Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networksrency and trust can be ensured in the underlying distributions and relationships of the resulting synthetic datasets. What is more, these datasets offer a strong level of privacy through lower risks of identifying real patients.作者: inferno 時(shí)間: 2025-3-23 21:21 作者: 縮減了 時(shí)間: 2025-3-23 22:29 作者: GIST 時(shí)間: 2025-3-24 05:53
Resource-Constrained On-Device Learning by Dynamic Averagingr processors typically only have integer processing capabilities. This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power processors, is privacy-preserving, and effectively minimizes communication. The empiri作者: Hyperalgesia 時(shí)間: 2025-3-24 08:50
MitM Attack Detection in BLE Networks Using Reconstruction and Classification Machine Learning Technus data batches. We then applied a classification method based on Text-CNN technique to classify packets as normal or attack inside each suspicious batch. Our model reconstruction results show that we are able to discriminate normal and attack models with high precision and our classification method作者: jocular 時(shí)間: 2025-3-24 14:27
Hybrid Connection and Host Clustering for Community Detection in Spatial-Temporal Network Datay unsupervised, our method outperforms many existing approaches. To the best of our knowledge, the only approaches with comparable performance require manual filtering of connections and feature engineering steps. In contrast, our method is applied to raw network traffic. We apply our pipeline to th作者: G-spot 時(shí)間: 2025-3-24 18:38 作者: evince 時(shí)間: 2025-3-24 22:16
Conference proceedings 2020DML 2020,?Second? International Workshop? on? eXplainable? Knowledge? Discovery in? Data Mining, XKDD 2020; 8th?International Workshop on News Recommendation and Analytics, INRA 2020.?.The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attributio作者: Narcissist 時(shí)間: 2025-3-25 00:10
1865-0929 ws Recommendation and Analytics, INRA 2020.?.The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attributio978-3-030-65964-6978-3-030-65965-3Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: Mammal 時(shí)間: 2025-3-25 05:42 作者: commute 時(shí)間: 2025-3-25 08:41
Psychosomatic Models of Development is the primary factor explaining why job-seekers select certain jobs. In practice, job seeker behavior is much more complex and there are other factors that should be considered. In this paper, we therefore propose the . which considers salary satisfaction, topic preference matching, and accessibil作者: 增減字母法 時(shí)間: 2025-3-25 15:41 作者: Fecundity 時(shí)間: 2025-3-25 19:16 作者: 聯(lián)想記憶 時(shí)間: 2025-3-25 20:05 作者: murmur 時(shí)間: 2025-3-26 00:48 作者: Genome 時(shí)間: 2025-3-26 06:39
Arndt Heilmann,Carme Llorca-Bofíply of funds, banks would act as the conduit between the funds and the borrowers. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Kiva, in particular, allows lenders to fund projects 作者: 熄滅 時(shí)間: 2025-3-26 12:16
The Euthyphro Problem Revisited,the community focuses on the issue of the social fairness of machine learning systems, we suggest that another relevant aspect of this debate concerns the political implications of the decision of using machine learning systems. Relying on the theory of Left realism, we argue that, from several poin作者: Offstage 時(shí)間: 2025-3-26 14:09 作者: biopsy 時(shí)間: 2025-3-26 19:51 作者: Myosin 時(shí)間: 2025-3-26 21:12 作者: Conduit 時(shí)間: 2025-3-27 01:11 作者: Anguish 時(shí)間: 2025-3-27 08:35 作者: committed 時(shí)間: 2025-3-27 12:33
Alan Park FRICS, MCIOB, ACIArb., FAFMly generated malware. Also, the signature, behavior, and anomaly-based defense mechanisms are susceptible to obfuscation and polymorphism attacks. With machine learning in practice, several authors proposed different classification and visualization techniques for malware detection. Images have prov作者: 兒童 時(shí)間: 2025-3-27 16:39 作者: Evocative 時(shí)間: 2025-3-27 19:31 作者: thalamus 時(shí)間: 2025-3-28 00:55
https://doi.org/10.1007/978-3-030-65965-3artificial intelligence; computer hardware; computer networks; computer security; computer systems; corre作者: gnarled 時(shí)間: 2025-3-28 02:57 作者: 背帶 時(shí)間: 2025-3-28 09:36
Communications in Computer and Information Sciencehttp://image.papertrans.cn/e/image/300279.jpg作者: Chauvinistic 時(shí)間: 2025-3-28 11:20 作者: 蝕刻 時(shí)間: 2025-3-28 18:02 作者: ESO 時(shí)間: 2025-3-28 21:41
Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networkse could be used to improve public health dramatically. To the growing AI in health industry, this data offers huge potential in generating markets for new technologies in healthcare. However, primary care data is extremely sensitive. It contains data on individuals that is of a highly personal natur作者: 進(jìn)步 時(shí)間: 2025-3-28 23:20 作者: 逢迎春日 時(shí)間: 2025-3-29 03:12 作者: circumvent 時(shí)間: 2025-3-29 07:56
Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.orgply of funds, banks would act as the conduit between the funds and the borrowers. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Kiva, in particular, allows lenders to fund projects 作者: 同時(shí)發(fā)生 時(shí)間: 2025-3-29 13:25
A Left Realist Critique of the Political Value of Adopting Machine Learning Systems in Criminal Justthe community focuses on the issue of the social fairness of machine learning systems, we suggest that another relevant aspect of this debate concerns the political implications of the decision of using machine learning systems. Relying on the theory of Left realism, we argue that, from several poin作者: 王得到 時(shí)間: 2025-3-29 16:46 作者: 陪審團(tuán)每個(gè)人 時(shí)間: 2025-3-29 20:01 作者: 我吃花盤旋 時(shí)間: 2025-3-30 00:33 作者: gain631 時(shí)間: 2025-3-30 06:24
Advocating for Multiple Defense Strategies Against Adversarial Examplesinst . adversarial examples and vice versa. In this paper we conduct a geometrical analysis that validates this observation. Then, we provide a number of empirical insights to illustrate the effect of this phenomenon in practice. Then, we review some of the existing defense mechanisms that attempt t作者: palpitate 時(shí)間: 2025-3-30 12:14 作者: Deduct 時(shí)間: 2025-3-30 13:48 作者: 使虛弱 時(shí)間: 2025-3-30 19:09
A Hybrid Recommendation System Based on Bidirectional Encoder Representationsad item descriptions during online shopping, which contain key information about the item and its features. However the item descriptions are in unstructured form and using them in the deep learning model is a problem. In this study, we integrate a pioneering Natural Language Processing technique in作者: gerrymander 時(shí)間: 2025-3-30 22:45
Leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logsdicting the activity that will be executed as the next step during process execution. However, traditional algorithms do not cope with the presence of parallel activities, thus failing to devise accurate prediction of multiple parallel activities that will be simultaneously executed. Moreover, they 作者: 能夠支付 時(shí)間: 2025-3-31 03:54
The Euthyphro Problem Revisited,is close to the law and order stance. Far from offering a political judgment of value, the aim of the paper is to raise awareness about the potential implicit, and often overlooked, political assumptions and political values that may be undergirding a decision that is apparently purely technical.作者: LEERY 時(shí)間: 2025-3-31 08:20 作者: Priapism 時(shí)間: 2025-3-31 10:59
https://doi.org/10.1007/978-3-662-07167-0ology, in which multi-target regression is used to predict the next parallel activities in event logs without the need of aligning traces during process executions. Experimental results show that the proposed solution achieve more accurate predictions compared to the single-target setting.
