Reviewerly/exHarmony huggingface dataset:
Evaluating Pre-training Strategies for Collaborative Filtering

Pre-training is essential for effective representation learning models, especially in natural language processing and computer vision-related tasks. The core idea is to learn representations, usually through unsupervised or self-supervised approaches on large and generic source datasets, and use those pre-trained representations (aka embeddings) as initial parameter values during training on the target dataset. Seminal works in this area show that pre-training can act as a regularization mechanism placing the model parameters in regions of the optimization landscape closer to better local minima than random parameter initialization. However, no systematic studies evaluate the effectiveness of pre-training strategies on model-based collaborative filtering. This paper conducts a broad set of experiments to evaluate different pre-training strategies for collaborative filtering using Matrix Factorization (MF) as the base model. We show that such models equipped with pre-training in a transfer learning setting can vastly improve the prediction quality compared to the standard random parameter initialization baseline, reaching state-of-the-art results in standard recommender systems benchmarks. We also present alternatives for the out-of-vocabulary item problem (i.e., items present in target but not in source datasets) and show that pre-training in the context of MF acts as a regularizer, explaining the improvement in model generalization.
Improving risk classification and ratemaking using mixture‐of‐experts models with random effects

Abstract In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit‐weighted Reduced Mixture‐of‐Experts , which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness‐of‐fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history.
Analisis Keefektivan Aplikasi Grab Menggunakan Metode Heuristic Evaluation

Advances in science and technology that are increasingly rapidly and developing in today's era make users get business opportunities through technology media. Such as the use of trading media, service companies use internet media, which is currently not difficult to reach by all groups. One of these services is an online transportation service, namely Grab. The grab application can make grab-car orders, go-rides and not only that on the grab application. There is a menu for ordering go-food, pulses/tokens, cinema tickets, hotels, bills and others. This study aims to determine the effectiveness of the grab application used by the people of Palembang City. This grab application has been seen as a facility that provides convenience and convenience in ordering online based on user experience. The convenience and practicality provided are of course the reasons people use Grab services. For this reason, it is necessary to evaluate usability so that users do not experience difficulties in accessing the application, and also users can make repeated purchases. This evaluation is needed to improve the quality of the website, especially in terms of user experience to make it better, therefore the grab party must pay attention to the ease of users in accessing the grab application. The ease of accessing the application cannot be separated from the usability of a website. The Heuristic Evaluation method is a method that can be used to identify and explore Usability problems in the system under study.Keywords : Grab Application, Usabilitty, Heuristic Evaluation
Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs.
Hierarchical Agglomerative Clustering to Detect Test Collusion on Computer‐Based Tests

Abstract There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering (HAC) for detecting aberrant test takers on Computer‐Based Testing (CBT) is explored. Random forest ensembles are used to evaluate the accuracy of the clustering and find the important features to classify the aberrant test takers. Testing data from a certification exam is used. The level of overlap between the exact response matches on incorrectly keyed items in the exam preparation material and HAC are compared. Integrating HAC as an investigation mean is promising in this field to improve the accuracy of classification of aberrant test takers.
Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective

The popularity of linear shallow autoencoders for collaborative filtering is growing in the research community, and internet industry providers of Recommender Systems are also taking notice. However, despite their simplicity and accuracy, these models often cannot be used in real-world industrial recommender systems due to their inability to scale to very large interaction matrices. Our research aims to address this issue by developing a scalable, explainable, and accurate shallow linear autoencoder method for collaborative filtering that meets the demands of real-world recommenders. In this paper, we present our industrial Ph.D. research project, which includes: (1) the development of a scalable method called ELSA and the adaptation of the method to a large real-world recommender and (2) the creation of a framework to visualize the recommender systems insights based on modeling the distribution of retrieval metrics in latent user space. We discuss the current status of our project, the key steps to finish the project, and the possible future extensions after the dissertation.
Combining Heterogeneous Embeddings for Knowledge-Aware Recommendation Models

In the last few years, Knowledge-Aware Recommender Systems (KARSs) got an increasing interest in the community thanks to their ability at encoding diverse and heterogeneous data sources, both structured (such as knowledge graphs) and unstructured (such as plain text). Indeed, as shown by several shreds of evidence, thanks to the combination of such information, KARSs are able to provide competitive performances in several scenarios. In particular, state-of-the-art KARSs leverage the current wave of deep learning and are able to process and exploit large corpora of information that provide complementary and useful characteristics of the items, including knowledge graphs, descriptive properties, reviews, text, and multimedia content. The objective of my Ph.D. is to investigate methods to design and develop knowledge-aware recommendation models based on the merging of heterogeneous embeddings. Based on the combination of diverse information sources, I plan to develop novel models able to provide accurate, fair, and explainable recommendations.
Atom-photon entanglement in a four-level coherent media via structure light

In this paper, we proposed a new model controlling the quantum entropy in a four-level quantum system via optical vortex structure light. The four-wave mixing (FWM) processes in the medium cause it to become phase-dependent. Therefore, the degree of entanglement (DEM) can be controlled via relative phase between applied lights when the deriving light becomes plane wave. However, for optical vortex of the deriving light, the spatial control of the DEM is possible via orbital angular momentum (OAM) of light. Our findings may have potential applications in the OAM-dependent devices in quantum information science.
Teaching pentesting to social sciences students using experiential learning techniques to improve attitudes towards possible cybersecurity careers

Labor market analysis shows that there is a significant shortage of experienced cybersecurity professionals, and this trend is expected to continue in the future. In addition, young people who are reluctant to choose STEM subjects in school typically do not see cybersecurity as a part of their future because they believe it demands exclusive technical knowledge that is beyond their reach. We aimed to change this perception among students of the social sciences, assuming that by providing social science students with the basics of cybersecurity, it would be possible to raise their awareness and encourage them to consider this field as a potential career option. Our team has designed a concise technical course based on Kolb's model that employs experiential learning to provide students with a basic knowledge of ethical intrusion (penetration testing). During the 32-hour subject, cadet officers with no prior IT education experienced all the steps of hacking both into a remotely accessible and physically accessible computer, including initial reconnaissance, vulnerability scanning, exploitation, and privilege escalation. A hands-on practical task of breaking into a highly vulnerable remote computer allowed for the evaluation of knowledge and skills as well as the reinforcement of learning experiences. In order to assess how the students' perceptions of the cybersecurity profession have changed based on the theory of planned behavior, they were asked to provide feedback immediately after the course and one year later. The results indicate that the short, technically challenging, but practical course based on experiential learning had a significant and positive effect on participants' attitudes: they were substantially more likely to consider cybersecurity as a future career, and some of them began participating in other cybersecurity courses or activities. It is reasonable to assume, therefore, that providing similar technical courses to social science students will encourage them to pursue cybersecurity-related careers in the future.
Robustness assessment of hyperspectral image CNNs using metamorphic testing

Remote sensing has proven its utility in many critical domains, such as medicine, military, and ecology. Recently, we have been witnessing a surge in the adoption of deep learning (DL) techniques by the remote sensing community. DL-based classifiers, such as convolutional neural networks (CNNs), have been reported to achieve impressive predictive performances reaching 99% of accuracy when applied to hyperspectral images (HSIs), a high-dimensional type of remote sensing data. However, these deep learners are known to be highly sensitive to even slight perturbations of their high-dimensional raw inputs. In real-world contexts, concerns can be raised about how robust they really are against corner-case scenarios. When HSI classifiers are applied in safety–critical applications, ensuring an adequate level of robustness is crucial to prevent unexpected system behaviors. Yet, there are few studies dealing with their robustness, nor are RGB-testing methods able to cover the HSI-specific challenges. This led us to propose a systematic testing method to assess the robustness of the CNNs trained to classify HSIs. First, we elaborate domain-specific metamorphic transformations that simulate naturally-occurring distortions of remote sensing HSIs. Then, we leverage metaheuristic search algorithms to optimize the fitness of synthetically-distorted inputs to stress the weaknesses of the on-testing CNN, while remaining in compliance with domain expert requirements, in order to preserve the semantic of the generated inputs. Relying on our metamorphic testing method, we assess the robustness of established and novel CNNs for HSI classification, and demonstrate their failure, on average, in 25% of the produced test cases. Furthermore, we fine-tuned the tested CNNs on training data augmented with these failure-revealing metamorphic transformations. Results show that the fined-tuning successfully fixed at least 90% of the CNN weaknesses, with less than 1% of degradation in the original predictive performance, outperforming the common iterative gradient-based adversarial attack, namely, Projected Gradient Descent (PGD).
Pruning-and-distillation: One-stage joint compression framework for CNNs via clustering

Network pruning and knowledge distillation, as two effective network compression techniques, have drawn extensive attention due to their success in reducing model complexity. However, previous works regard them as two independent methods and combine them in an isolated manner rather than joint, leading to a sub-optimal optimization. In this paper, we propose a collaborative compression scheme named Pruning-and-Distillation via Clustering (PDC), which integrates pruning and distillation into an end-to-end single-stage framework that takes both advantages of them. Specifically, instead of directly deleting or zeroing out unimportant filters within each layer, we reconstruct them based on clustering, which preserves the learned features as much as possible. The guidance from the teacher is integrated into the pruning process to further improve the generalization of pruned model, which alleviates the randomness caused by reconstruction to some extent. After convergence, we can equivalently remove reconstructed filters within each cluster through the proposed channel addition operation. Benefiting from such equivalence, we no longer require the time-consuming fine-tuning step to regain accuracy. Extensive experiments on CIFAR-10/100 and ImageNet datasets show that our method achieves the best trade-off between performance and complexity compared with other state-of-the-art algorithms. For example, for ResNet-110, we achieve a 61.5% FLOPs reduction with even 0.14% top-1 accuracy increase on CIFAR-10 and remove 55.2% FLOPs with only 0.32% accuracy drop on CIFAR-100.
Parallel approaches to extract multi-level high utility itemsets from hierarchical transaction databases

In the field of data mining, high utility itemset mining (HUIM) is a relevant mining task, with the aim of analyzing customer transaction databases. HUIM consists of exploiting the set of items that are often purchased together and yield high profit value. In real-world applications, transaction databases often come with item categorization, stored in a taxonomy. Items in these databases can be clustered into specific categories at higher levels of abstraction. Extracting and analyzing itemsets discovered from different levels of abstraction can provide more useful insights into customer behaviors. However, considering item taxonomy increases the problem’s complexity, hence prolonging the execution time needed to explore the search space. Parallelism is thus employed to address this drawback, but previous approaches are not efficient as they only adopt simple scheduling strategies or do not utilize the full capabilities of a multi-core processor. This work introduces three new efficient strategies to significantly boost the performance of the multi-level high utility itemset mining task using multi-core processing. Two new algorithms, called MCML+ and MCML++, are also proposed by adopting the suggested strategies. Extensive experiments on several large databases show that our proposed algorithms have better performance compared to previous approaches in terms of running time and scalability, up to 4.0 times better than the previous parallelized algorithm, the MCML-Miner algorithm; and over 9.0 times faster than the original sequential algorithm, the MLHUI-Miner algorithm.
GSM-Net: A global sequence modelling network for the segmentation of short axis CINE MRI images

Atrial Fibrillation (AF) is a disease where the atria fail to properly contract but quiver instead, due to the abnormal electrical activity of the atrial tissue. In AF patients, anatomical and functional parameters of the left atrium (LA) largely differ from that of healthy people due to LA remodelling, which can continue in many cases after the catheter ablation treatment. Therefore, it is important to follow up with AF patients to detect any recurrence. LA segmentation masks obtained from short-axis CINE MRI images are used as the gold standard for the quantification of LA parameters. Thick slices of CINE MRI images hinder the use of 3D networks for segmentation while 2D architectures often fail to model inter-slice dependencies. This study presents GSM-Net which approximates 3D networks with effective modelling of inter-slice similarities with two new modules: global slice sequence encoder (GSSE) and sequence dependent channel attention module (SdCAt). In contrast to previous work modelling only local inter-slice similarities, GSSE also models global spatial dependencies across slices. SdCAt generates a distribution of attention weights over MRI slices per channel, to better trace characteristic changes in the size of the LA or other structures across slices. We found that GSM-Net outperforms previous methods on LA segmentation and helps to identify AF recurrence patients. We believe that GSM-Net can be used as an automatic tool to estimate LA parameters such as ejection fraction to identify AF, and to follow up with patients after treatment to detect any recurrence.
Students' Satisfaction with the Use of Google Forms for the TOEIC Test to Evaluate English Proficiency

The purposes of this study were to investigate students' satisfaction with the use of Google Forms for the TOEIC test to evaluate English proficiency, and to study additional suggestions in order to improve the methods of teaching and evaluating students. The population of this study was 621 second-year students who enrolled in the TOEIC preparation course in the second semester of the 2022 academic year. The samples of this study were 150 students derived through the Simple Random Sampling Technique. The research instrument used in this study was a rating scale and an open-ended questionnaire. Frequency, percentage, mean, standard deviation, and content analysis were used for data analysis. The research findings were 1): students had the highest level of satisfaction in Accessibility, followed by Benefits and Usability for the TOEIC Test; and 2) the additional opinions and suggestions were listed based on the benefits and drawbacks of Google forms. Overall, there were 37 students (25%) who possess positive comments about using Google Forms and 21 students (13.33%) who are not satisfied with it.
YouTube Search Engine: Most Popular Content between Myth and Horror in Indonesia

YouTube is a video-audio based social platform that offers a variety of content. In Indonesia, entertainment content is one of the things that are in high demand. Popular entertainment content is horror and myth. This is related to the beliefs of the Indonesian people regarding myths. This article aims to analyze horror and myth content that ranks at the top of the YouTube search engine based on creators and content types. Online data retrieval in the form of YouTube content using keywords horror and myth taken from search engines. Search engine is a feature to search content based on certain keywords. This research found that the top-ranking content in the YouTube search engine is dominated by creators with verified accounts and most types of content such as storytelling and listicles. The results of this study provide recommendations for creators and the media industry in the field of horror and myth content production in making content optimally.
Business Intelligence Dashboard for Asset Inventory Management Monitoring at PT Indonesia Power Head Office

Currently the development of information technology is so fast, the use of information technology is absolutely necessary to support business processes and increase the productivity of the company. Likewise with PT. Indonesia Power where the company's business development is currently technology-based. This causes the number of software and laptops to increase so that the recording and management of software, licenses and devices are still not going well because currently recording software licenses still use document files. This is felt to be less effective because tracing a license takes longer and licenses that have expired are still installed on employee laptops because of not being properly monitored. In addition to poor record keeping, several users use software that is not officially licensed within PT. Indonesia Power so that it can harm the company. Therefore, a Business Intelligence (BI) Dashboard is needed using the Prototype method which provides monitoring information related to IT software asset inventory with the aim that the monitoring process can be carried out more effectively and efficiently. It is hoped that this research will produce a BI dashboard to assist the IT sector in monitoring IT software asset inventory.