Reviewerly/exHarmony huggingface dataset:
Cache-Enhanced InBatch Sampling with Difficulty-Based Replacement Strategies for Learning Recommenders

Negative sampling techniques are prevalent in learning recommenders to reduce the computational cost over the entire corpus, but existing methods still have a significant overhead for re-encoding out-of-batch items. Inbatch sampling is a more practical strategy that regards items in the mini-batch as negatives, although it suffers from exposure bias. Several researchers attempt to alleviate the bias by cache mechanism, which supplements more items for better approximation, but none of them sufficiently evaluate the information level of different items and further exploit them. In this paper, we propose a Cache-Enhanced InBatch Sampling with Difficulty-Based Replacement Strategy for Learning Recommenders that heuristically and adaptively updates the cache depending on the designed training difficulty of negative samples. Specifically, the cache is updated based on the average and standard deviation with respect to the training difficulty, which correspond with the estimated first-order and second-order moments, in which way the items with high averages and high uncertainties have a higher probability of being restored. Thus, the historical informative items in training are more effectively explored and exploited, leading to superior and rapid convergence. The proposed DBRS is evaluated on four real-world datasets and outperforms the existing state-of-the-art approaches.
Data from Whole Slide Imaging-Based Prediction of &lt;i&gt;TP53&lt;/i&gt; Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

&lt;div&gt;Abstract&lt;p&gt;In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated &lt;i&gt;TP53&lt;/i&gt; mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of &lt;i&gt;TP53&lt;/i&gt; mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed &lt;i&gt;TiDo&lt;/i&gt;, a deep learning model that achieves state-of-the-art performance in predicting &lt;i&gt;TP53&lt;/i&gt; mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by &lt;i&gt;TP53&lt;/i&gt; deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as &lt;i&gt;TP53&lt;/i&gt; mutations. Comparative expression and histologic cell type analyses identified a &lt;i&gt;TP53&lt;/i&gt;-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual &lt;i&gt;TP53&lt;/i&gt; mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers.&lt;/p&gt;Significance:&lt;p&gt;Deep learning models predicting &lt;i&gt;TP53&lt;/i&gt; mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as &lt;i&gt;in silico&lt;/i&gt; prognostic biomarkers.&lt;/p&gt;&lt;p&gt;&lt;i&gt;&lt;a href="https://aacrjournals.org/cancerres/article/doi/10.1158/0008-5472.CAN-23-1856" target="_blank"&gt;See related commentary by Bordeleau, p. 2809&lt;/a&gt;&lt;/i&gt;&lt;/p&gt;&lt;/div&gt;
Student Attitudes Toward Basic Characteristics of Mobile Learning

Mobile learning has a number of positive characteristics that define it as a new pedagogical concept suitable for the mobile era. This article reflects students' attitudes towards some of the basic characteristics of mobile learning.
Research on Chord Generation in Automated Music Composition Using Deep Learning Algorithms

With the development of technology, automated music composition has received widespread attention in music creation. This article mainly focuses on the generation of chords in automated music composition. First, relevant music knowledge was briefly introduced, and then the composition of the Transformer model was explained. A two-layer bidirectional Transformer method was designed to generate chords for the main melody and chorus separately, followed by the establishment of chord coloring and sound production models. Ten music professionals and 40 ordinary college students compared the coherence, pleasantness, and innovation of the chords generated by Hidden Markov Model (HMM), Long Short-Term Memory (LSTM), and the method proposed in this paper. The results showed that the chord generated by the method proposed in this paper achieved higher scores in the evaluation. Overall, the scores given by the music professionals and ordinary college students were 3.64 and 3.91, respectively, which were higher than those of the HMM and LSTM methods. The experimental results prove the superiority of the chord generation method proposed in this paper. The method can be applied to automated music composition.
Person Re-Identification Using Local Relation-Aware Graph Convolutional Network

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian images. As a result, local features contain limited information from one pedestrian image, and cannot benefit from other pedestrian images. In this paper, we propose a novel approach named Local Relation-Aware Graph Convolutional Network (LRGCN) to learn the relationship of local features among different pedestrian images. In order to completely describe the relationship of local features among different pedestrian images, we propose overlap graph and similarity graph. The overlap graph formulates the edge weight as the overlap node number in the node's neighborhoods so as to learn robust local features, and the similarity graph defines the edge weight as the similarity between the nodes to learn discriminative local features. To propagate the information for different kinds of nodes effectively, we propose the Structural Graph Convolution (SGConv) operation. Different from traditional graph convolution operations where all nodes share the same parameter matrix, SGConv learns different parameter matrices for the node itself and its neighbor nodes to improve the expressive power. We conduct comprehensive experiments to verify our method on four large-scale person re-ID databases, and the overall results show LRGCN exceeds the state-of-the-art methods.
Editing Motion Graphics Video via Motion Vectorization and Transformation

Motion graphics videos are widely used in Web design, digital advertising, animated logos and film title sequences, to capture a viewer's attention. But editing such video is challenging because the video provides a low-level sequence of pixels and frames rather than higher-level structure such as the objects in the video with their corresponding motions and occlusions. We present a motion vectorization pipeline for converting motion graphics video into an SVG motion program that provides such structure. The resulting SVG program can be rendered using any SVG renderer(e.g. most Web browsers) and edited using any SVG editor. We also introduce a program transformation API that facilitates editing of a SVG motion program to create variations that adjust the timing, motions and/or appearances of objects. We show how the API can be used to create a variety of effects including retiming object motion to match a music beat, adding motion textures to objects, and collision preserving appearance changes.
BridgeHand2Vec Bridge Hand Representation

Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods. This paper proposes the BridgeHand2Vec approach, which leverages a neural network to embed a bridge player’s hand (consisting of 13 cards) into a vector space. The resulting representation reflects the strength of the hand in the game and enables interpretable distances to be determined between different hands. This representation is derived by training a neural network to estimate the number of tricks that a pair of players can take. In the remainder of this paper, we analyze the properties of the resulting vector space and provide examples of its application in reinforcement learning, and opening bid classification. Although this was not our main goal, the neural network used for the vectorization achieves SOTA results on the DDBP2 problem (estimating the number of tricks for two given hands).
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations

The performance of classification models depends on the quality of training data labels. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive. To address this challenge, active learning algorithms are commonly employed to select only the most relevant data for labeling. However, this is possible only when the quality and quantity of labels acquired from experts are sufficient. In many applications, a trade-off between annotating individual samples by multiple annotators to increase label quality vs. annotating new samples to increase the total number of labeled instances is necessary. In this paper, we address the issue of faulty data annotations in the context of active learning. In particular, we propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space. The proposed methods require little to no intersection between samples annotated by different experts. Our experiments on four public datasets indicate the robustness and superiority of the proposed methods in both, the estimation of the annotator’s reliability, and the assignment of actual labels, against the state-of-the-art algorithms and the simple majority voting.
Implementasi Sistem Informasi Manajemen Aplikasi Pesona Tanah Datar Pada Dinas Pariwisata Pemuda Dan Olahraga Kabupaten Tanah Datar

The management information system in the form of the “Pesona Tanah Datar” application is part of Digital Governance which aims to realize good governance by utilizing technology. Modern tourism really needs technology for marketing, documentation and data collection which is very important to support the development of tourism as well as providing information services to the public regarding tourism, youth and sports in Tanah Datar district. applicable tourism information to support and develop regional tourism. This tourism information system will provide optimal value benefits when used in conjunction with a good tourism website and tourism multimedia. This study aims to determine the Implementation of the Pesona Tanah Datar Application Management Information System at the Youth and Sports Office of Tanah Datar Regency. In this study the authors used a descriptive qualitative research type. Data collection was not guided by theory, but guided by facts found in the field. Therefore, the data analysis carried out is inductive based on the facts found and can then be constructed into hypotheses and theories. The results showed that the implementation of a flat land charm management information system at the Department of Tourism, Youth and Sports went well.
Noisy image segmentation based on synchronous dynamics of coupled photonic spiking neurons

The collective dynamics in neural networks is essential for information processing and has attracted much interest on the application in artificial intelligence. Synchronization is one of the most dominant phenomenon in the collective dynamics of neural network. Here, we propose to use the spiking dynamics and collective synchronization of coupled photonic spiking neurons for noisy image segmentation. Based on the synchronization mechanism and synchronization control, the noised pattern segmentation is demonstrated numerically. This work provides insight into the possible application based on the collective dynamics of large-scale photonic networks and opens a way for ultra-high speed image processing.
Multi-Agent Cooperative Games Using Belief Map Assisted Training

In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model’s complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent’s hidden state to provide a symbolic representation of the agent’s understanding of the environment and other agents’ status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in the game. We evaluate BAMS’s performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average.
Random Ensemble of Extended CNN Structures for Medical Image Recognition

The paper presents a new approach for creating an ensemble of deep neural CNN networks for medical image recognition. The idea is to add the extra layer of a limited number of neurons to two different CNN structures. The added layers in both networks are fully connected to the previous flattened layer of the CNN. The outputs of these two layers are randomly mixed to form the set that supplies the final classifier. Averaging the results of many repetitions of such a procedure with random weights provides the final classification decision. Such a form of signal processing is equivalent to implicit regularization. The proposed system has been applied to the recognition of images representing two classes of USG images of lymph nodes. The extensive experiments have shown an advantage over the results obtained by using the same standard CNN structures. The proposed method can be easily adapted to other deep network structures and different tasks.
Learning Image-Adaptive Lookup Tables With Spatial Awareness for Image Harmonization

Image harmonization aims to address inharmony between the foreground and background of composite images. Current methods mainly adopt the encoder-decoder reconstruction architecture, which restricts their application to high-resolution images due to heavy resource cost. Inspired by Photoshop, whether we can leverage some adjustment tools to apply on composite images to generate harmonized ones. This paper gives an answer "yes", and explores a new viewpoint to address image harmonization problem by lookup tables (LUTs) rather than reconstruct full images, which saves a lot of memory. Specifically, a deep model is utilized to encode the down-sampled version of the input image as style-relevant weights to fuse multiple basic LUTs into image-adaptive ones, which are employed to transform the global color of the foreground in source high-resolution composite image to be harmonious with the background. Besides, a spatial attention module is applied to learn the spatial map for revising preliminary LUTs transformation as well as raising the local spatial awareness of the image-adaptive LUTs. Extensive experiments show that our method achieves competitive performance both qualitatively and quantitatively with a smaller model size.
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation

Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortunately, this strategy requires costly and time-consuming pixel-level annotations. To overcome this problem, this paper proposes MaskGAN, a novel and cost-effective framework that enforces structural consistency by utilizing automatically extracted coarse masks. Our approach employs a mask generator to outline anatomical structures and a content generator to synthesize CT contents that align with these structures. Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children. Specifically, MaskGAN excels in preserving anatomical structures without the need for expert annotations. The code for this paper can be found at https://github.com/HieuPhan33/MaskGAN .
Materials from the re3data COREF / CoreTrustSeal Workshop on Quality Management at Research Data Repositories

The objective of the workshop was to approach the pervasive, yet elusive topic of data quality management from several angles. To ensure a common frame of reference for the workshop, contributions were guided by the Framework for Quality Assurance of Data Publications at Research Data Repositories that was developed as part of a PhD project.
Releasing version 4.0 of the re3data Metadata Schema

re3data caters to a range of needs for diverse stakeholders (Vierkant et al., 2021). Detailed and precise repository descriptions are at the heart of the majority of these use cases, and repository metadata are given special attention at the re3data registry.
KAJIAN KARAKTERISTIK LAHAN UNTUK MENDUKUNG PENYUSUNAN MODEL DECISION SUPPORT SYSTEM (DSS) OPTIMALISASI LAHAN DI KABUPATEN PANGKEP

&#x0D; The biophysical characteristics and environmental conditions of the land are required for the development of a Decision Support System (DSS) model for land optimization. The purpose of this study was to examine the characteristics of land cover, and the biophysical and environmental conditions of the land in Pangkep Regency. Suboptimal land identification and analysis of land and climate characteristics are carried out on the basis of secondary data obtained from satellite images, both land cover data in 2022, data in the form of three-dimensional DEM (Digital Elevation Model), and land characteristic data in the form of land system maps produced by the Geospatial Information Agency. The land cover in the Pangkep Regency area consists of fourteen types of land cover dominated by jungles, rice fields, and ponds. Flat topography with slopes of 0-2% and geological formations of alluvium and coastal deposits, Tonasa Formation, and Sekala Formation are dominant in Pangkep Regency. The dominant soil characteristic in this region is a lithosol soil type, soil depth more than 150 cm, soil pH is slightly acidic (5.6-6.5), clay texture, soil drainage criteria are inhibited and good, soil salinity in the range of 0.33%, C-Organic content with very low and low criteria, medium soil CEC, low total N value with a range of 0.1–0.21% and medium with 0.22–0.51%, Soil potassium content including very low criteria (&lt;10 mg/100g) is in the range of 0.05 – 0.2 mg/100g and dominant 0.2 mg/100g, and rock abundance is 100+. The rainfall in this region is predominantly 2500 mm.th-1 and the average temperature is 23 - 32°C.
Artificial Bee Colony Algorithm Based on Quantum Bloch Spherical Optimization

The swarm intelligence optimization algorithm has strong adaptability to optimization problems, fast computational speed, and the ability to quickly find the optimal solution, demonstrating a momentum of rapid development. As a type of swarm intelligence optimization algorithm, artificial bee colony algorithm obtains the optimal solution through the cyclic foraging and iteration of hired bees, observation bees, and reconnaissance bees, and has now been widely applied in various fields, But the artificial bee colony algorithm has the shortcomings of slow Rate of convergence and easy to fall into the local optimal solution. To overcome these shortcomings, this paper proposes an artificial bee colony algorithm based on the quantum Bloch spherical optimization mechanism. The effectiveness of the algorithm is proved through six benchmark test functions. The experimental results of the convergence curve graph can show that the Rate of convergence is greatly accelerated, and the global optimal solution can be obtained quickly.
Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters

The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics.The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain.Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states.Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use.
Techno-Economic Appraisal of A Rooftop Solar Pv Project for A University Campus

In order to address environmental challenges, particularly climate change, and lessen institutions' carbon footprints, sustainable energy sources are deemed essential. Universities are dealing with rising electricity bills due to increasing student enrolments and multitude operations. Introducing a solar PV system would help to reduce universities carbon footprint and subsequently the annual electricity bill. This study aims to conduct a techno-economic assessment of installing a grid-connected solar Photovoltaic (PV) power generation system at the Universiti Teknologi MARA (UiTM) campus in Shah Alam. The PV-syst software were used to design and analyse the PV system for suitable building rooftops in Shah Alam campus, followed by the cost-benefit analyses. This study suggests an 8MW solar PV system for UiTM Shah Alam, with an MYR28,786,907 installation cost, and MYR0.298/kWh Levelized Cost of Electricity (LCOE) with a total payback period of 8 years, avoiding a total of 149,195 tonnes CO2 over the project lifetime. The Net Present Value, NPV is MYR 16,725,250.79, demonstrating that the PV system is a financially viable and profitable long-term investment. While comparing between the two mode of payments, the outright purchase has higher NPV in comparison to the Solar Power Purchase Agreement (SPPA) of zero capital expenditure, indicating better return. The UiTM Shah Alam solar PV system proposal has the potential to considerably lessen the university's reliance on non-renewable energy sources while decreasing its electricity costs, making it a financially feasible and sustainable alternative.