Reviewerly/exHarmony huggingface dataset:
Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development

Authors:
raw_author_name: Igor Zingman
raw_affiliation_string: Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH and Co., Biberach an der Riß, Germany
raw_author_name: Charlotte Lempp
raw_affiliation_string: Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH and Co., Biberach an der Riß, Germany
raw_author_name: Fabian Heinemann
raw_affiliation_string: Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH and Co., Biberach an der Riß, Germany

Abstract: We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.
Genetic Programming for Document Classification: A Transductive Transfer Learning System

Authors:
raw_author_name: Bing Xue
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Xiaoying Gao
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Mengjie Zhang
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

Abstract: Document classification is a challenging task to the data being high-dimensional and sparse. Many transfer learning methods have been investigated for improving the classification performance by effectively transferring knowledge from a source domain to a target domain, which is similar to but different from the source domain. However, most of the existing methods cannot handle the case that the training data of the target domain does not have labels. In this study, we propose a transductive transfer learning system, utilizing solutions evolved by genetic programming (GP) on a source domain to automatically pseudolabel the training data in the target domain in order to train classifiers. Different from many other transfer learning techniques, the proposed system pseudolabels target-domain training data to retrains classifiers using all target-domain features. The proposed method is examined on nine transfer learning tasks, and the results show that the proposed transductive GP system has better prediction accuracy on the test data in the target domain than existing transfer learning approaches including subspace alignment-domain adaptation methods, feature-level-domain adaptation methods, and one latest pseudolabeling strategy-based method.
Updating of higher education curriculum for future engineers during the COVID-19 pandemic

Authors:
raw_author_name: Olga V. Galustyan
raw_affiliation_string: Southern Federal University
raw_author_name: Galina P. Zhirkova
raw_affiliation_string: Saint Petersburg National Research University of Information Technologies, Mechanics and Optics

Abstract: Higher education is immersed in unpredictable environments and is facing challenges during the Coronavirus Disease 2019 (COVID-19) pandemic. It needs to adapt to external changes constantly, especially to the level of development of science and technology as the most important component of education courses for future engineers are facing development difficulties within achieving their goals as the core of higher education. Curriculum serves as basis for realization of goals of higher education and curriculum for future engineers needs updating. The paper analyzes dilemma of updating of higher education curriculum for future engineers within its content and structure. During the learning process students participated in curriculum renewal and accelerated the upgrading of curriculum content and structure. Finishing the course, students conducted reflection, improved their understanding during the reflection process continuously that helped them to develop the habit of lifelong learning. The authors propose to expand teaching methods for the courses, to adapt engineering courses to the needs of economic development during the COVID-19 pandemic, to strengthen students’ interaction and participation, to supply multiple participation in curriculum “customization” countermeasures to update higher education curriculum for future engineers.
The role of gamification implementation in improving quality and intention in software engineering learning

Authors:
raw_author_name: Tri Wahyuningsih
raw_affiliation_string: Doctoral Program of Computer Science, Universitas Kristen Satya Wacana, Salatiga
raw_author_name: Eko Sediyono
raw_affiliation_string: Doctoral Program of Computer Science, Universitas Kristen Satya Wacana, Salatiga
raw_author_name: Kristoko Dwi Hartomo
raw_affiliation_string: Doctoral Program of Computer Science, Universitas Kristen Satya Wacana, Salatiga
raw_author_name: Irwan Sembiring
raw_affiliation_string: Doctoral Program of Computer Science, Universitas Kristen Satya Wacana, Salatiga

Abstract: Gamification can make learning more fun and engaging for students. Software engineering can utilize gamification to help students learn and improve their skills from the complexity of software engineering. This study used quantitative research to examines perceived ease of use, student satisfaction, and perceived usefulness to measure gamification quality, which can have an impact on software engineering intention, namely intention, loyalty, and participation in following and understanding software engineering materials. The data was collected based on an online questionnaire survey, 90 data were collected and then measured and analyzed using SmartPLS 3. The results showed that perceived ease of use, student satisfaction, and perceived usefulness have a significant influence on gamification quality, which also leads to a positive impact on software engineering intention. This research guides teachers and educational institutions that gamification is very successful as a learning medium to simplify complex information to be more interactive.
TestFit: A plug-and-play one-pass test time method for medical image segmentation

Authors:
raw_author_name: Yizhe Zhang
raw_affiliation_string: Nanjing University of Science and Technology, Jiangsu 210094, China
raw_author_name: Tao Zhou
raw_affiliation_string: Nanjing University of Science and Technology, Jiangsu 210094, China
raw_author_name: Shuo Wang
raw_affiliation_string: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
raw_author_name: Ye Wu
raw_affiliation_string: Nanjing University of Science and Technology, Jiangsu 210094, China
raw_author_name: Pengfei Gu
raw_affiliation_string: University of Notre Dame, IN 46556, USA
raw_author_name: Qiang Chen
raw_affiliation_string: Nanjing University of Science and Technology, Jiangsu 210094, China
raw_author_name: Danny Z. Chen
raw_affiliation_string: University of Notre Dame, IN 46556, USA

Abstract: Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample—the same as the traditional test model pipeline—and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.
Heterogeneous Sensor Fault Detection for Networked Systems Based on a Graph Transformer

Authors:
raw_author_name: Aiping Pang
raw_affiliation_string: School of Electrical Engineering, Guizhou University, Guiyang, China
raw_author_name: Wen Yang
raw_affiliation_string: Key Laboratory of Space Launching Site Reliability Technology, Xichang Satelliate Launch Center, Xichang, China

Abstract: Networked systems like fuel, petrochemical, and water supply systems use various sensors for monitoring and control. Due to harsh environmental conditions, sensors are prone to failure and pose a significant threat to the stability of the system, making the accurate identification of such anomalies crucial. In recent years, data-driven sensor fault detection methods utilized sensor data correlations, spatial and temporal features, but they haven’t fully harnessed the physical structure relation of sensors in real system. Our study introduces a novel sensor fault detection method that integrates graph neural networks and transformers. The model efficiently uses the interdependence among various sensors for information transmission and aggregation, relying on the physical struction of networked system. To enhance the information exchange among identical sensor, the model devides the graph neural network into subgraphs based on sensor types. Within each subgraph, the attention mechanism of transformers is employed to capture sensor features and distributions, thereby improving the learning of the sensor node representation vector. Finally, a decoder with reconstructed input is utilized for sensor fault detection. The experiments demonstrate better fault detection accuracy in both real networked systems and four heterogeneous sensor datasets. Across all experimental cases, the F1 score of our method is 95.79%, and the AUC value is 98.83%. Finally, through ablation study, the effectiveness of each component of the model is confirmed.
Causal Inference in Recommender Systems: A Survey and Future Directions

Authors:
raw_author_name: Chen Gao
raw_affiliation_string: Beijing National Research Center for Information Science and Technology, Tsinghua University, China
raw_author_name: Yu Zheng
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, China
raw_author_name: Wenjie Wang
raw_affiliation_string: School of Computing, National University of Singapore, Singapore
raw_author_name: Fuli Feng
raw_affiliation_string: School of Information Science and Technology, University of Science and Technology of China, China
raw_author_name: Xiangnan He
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, China
raw_author_name: Yong Li
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, China

Abstract: Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality , not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.
A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Authors:
raw_author_name: Nada Badr Jarah
raw_author_name: Abbas Hanon Hassin Alasadi
raw_author_name: Kadhim Mahdi Hashim

Abstract: Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.
Data on investigation of potential gold mineralization sites in Tchollire and Environs (North Cameroon)

Authors:
raw_author_name: Quentin Marc Anaba Fotze
raw_affiliation_string: Centre for Geological and Mining Research, P.O. Box 333, Garoua, Cameroon; Department of Earth Sciences, Faculty of Science, University of Maroua, P.O. Box 814, Maroua, Cameroon
raw_author_name: Marcelin Bikoro Bi-Alou
raw_affiliation_string: Department of Earth Sciences, Faculty of Science, University of Maroua, P.O. Box 814, Maroua, Cameroon
raw_author_name: Théophile Ndougsa Mbarga
raw_affiliation_string: Department of Physics, Advanced Teacher's Training College, University of Yaoundé I, P.O. Box 47, Yaoundé, Cameroon
raw_author_name: Jean Paul Sep Nlomngan
raw_affiliation_string: Centre for Geological and Mining Research, P.O. Box 333, Garoua, Cameroon

Abstract: Fresh and altered rock samples were collected and analyzed during field and laboratory studies in Tchollire and Environs. This approach aimed at the delineation of hydrothermal alteration minerals which according to the geological and mining settings of Tchollire and Environs, may be associated with gold mineralizations sites. Field investigations were achieved during the dry season to ensure the representativeness and reliability of our samples whose collections were constrained by earlier remote sensing and geophysical studies as expressed in [1]. An optical microscope both in reflected and transmitted light was used for petrographic analyses of thin and polished sections of rock samples. Other rock samples were prepared for spectral measurements which were achieved using an analytical spectral device spectrometer. The data presented here are further interpreted and discussed in [1].
Improving the accuracy of predicted first-arrival times using deep learning

Authors:
raw_author_name: Yitao Pu
raw_affiliation_string: University of Alabama, Department of Geological Sciences, Tuscaloosa, Alabama, USA.
raw_author_name: Bo Zhang
raw_affiliation_string: University of Alabama, Department of Geological Sciences, Tuscaloosa, Alabama, USA. (corresponding author)
raw_author_name: Chenglin Wei
raw_affiliation_string: China National Petroleum Corporation, Bureau of Geophysical Prospecting, Zhuozhou, China.
raw_author_name: Yingyu Xu
raw_affiliation_string: China National Petroleum Corporation, Bureau of Geophysical Prospecting, Zhuozhou, China.
raw_author_name: Hongfei Liu
raw_affiliation_string: China National Petroleum Corporation, Bureau of Geophysical Prospecting, Zhuozhou, China.

Abstract: Recently, the task of first-arrival time picking for seismic shot gathers has been treated as an image segmentation problem, and deep-learning (DL) algorithms have been successfully used to predict first-arrival times. Currently, researchers mainly focus on leveraging cutting-edge DL algorithms to improve the performance of DL in first-arrival picking. There are few publications addressing the quality control of the results predicted by DL. We develop a three-step workflow to improve the accuracy of first-arrival time detection computed using DL algorithms. First, we obtain three predicted results (generation I) by applying the holistically nested U-net (HU-net) to seismic shot gathers, the envelope of seismic shot gathers, and the cosine of the instantaneous phase of seismic shot gathers. Subsequently, we obtain generation II picking by statistically analyzing the predicted generation I picking. Finally, we treat the first-arrival picking task as a constrained path search problem and the generation II picking function as the constraints. The developed workflow is applied to real seismic surveys to demonstrate its effectiveness.
Analysis of Effective Strategies and Teacher Motivation in Improving Literacy and Numeracy at State Elementary School Besowo 5 Kediri: Findings and Implications

Authors:
raw_author_name: 'Alia Nur Husna Afandi
raw_affiliation_string: Pendidikan Dasar, Universitas Negeri Malang
raw_author_name: Supriyono Koes Handayanto
raw_affiliation_string: Pendidikan Dasar, Universitas Negeri Malang
raw_author_name: Shirly Rizki Kusumaningrum
raw_affiliation_string: Pendidikan Dasar, Universitas Negeri Malang

Abstract: State Elementary School Besowo 5, Kepung District, Kediri, is an elementary school that succeeded in achieving high literacy and numeracy results on the education report card and achieved recognition as the Best BOSP Performance in Kediri Regency, even though the school experienced infrastructure challenges such as limited road access and limited cellular signal. The aim of this research is to explore the challenges, strategies and collaborative efforts of teachers in improving students' literacy and numeracy skills. The method in this research uses case studies through observation, interviews and document analysis. The analysis technique of this research uses triangulation techniques. Through the use of educational report cards as a basis, SDN Besowo 5 teachers provide special additional hours by utilizing a variety of literacy and numeracy materials, implementing various learning strategies, including guidance on finding keywords, Polya steps, using innovative worksheet, involving students as peer tutors, and provide treatment according to students' abilities. Apart from that, teachers also collaborate. Teachers' motivation for collaboration includes, among other things, improving school achievement, developing knowledge, and strengthening the school's positive image in the eyes of the community.
Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods

Authors:
raw_author_name: Ali Al Kadhim
raw_affiliation_string: Florida State University, 77 Chieftan Way, Tallahassee, Florida, 32306-1058, UNITED STATES

Abstract: Abstract High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference ( LF2I ) approach that makes it possible to construct confidence sets with the p -value function and to use the same function to check the coverage explicitly at any given parameter point. Like LF2I , this extension yields provably valid confidence sets in parameter inference problems for which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology 3 3 Code to reproduce all of our results is available on https://github.com/AliAlkadhim/ALFFI . .
EXPLORING FACTORS ASSOCIATED WITH SEROLOGICAL RESPONSE TO SARS-COV-2 VACCINES IN INDIVIDUALS WITH INFLAMMATORY BOWEL DISEASE: INTERACTIVE VISUALIZATION METHODS FOR MULTIDIMENSIONAL DATA

Authors:
raw_author_name: Christopher Ma

Abstract: SARS-CoV-2 vaccine response may be reduced in seniors (age 65+) with inflammatory bowel disease (IBD) and those on anti-TNF therapy. A challenge of the COVID-19 era has been conveying rapid-evolving health information, especially for groups with higher risk of poor outcomes of COVID-19 such as seniors and immunocompromised individuals.