Reviewerly/exHarmony huggingface dataset:
Latent Graph Inference using Product Manifolds

Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows models to dynamically learn the intrinsic graph structure of problems where the connectivity patterns of data may not be directly accessible. In this work, we generalize the discrete Differentiable Graph Module (dDGM) for latent graph learning. The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated. By incorporating Riemannian geometry into the model and generating more complex embedding spaces, we can improve the performance of the latent graph inference system. In particular, we propose a computationally tractable approach to produce product manifolds of constant curvature model spaces that can encode latent features of varying structure. The latent representations mapped onto the inferred product manifold are used to compute richer similarity measures that are leveraged by the latent graph learning model to obtain optimized latent graphs. Moreover, the curvature of the product manifold is learned during training alongside the rest of the network parameters and based on the downstream task, rather than it being a static embedding space. Our novel approach is tested on a wide range of datasets, and outperforms the original dDGM model.
In-Hand 3D Object Scanning from an RGB Sequence

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses.
The Vanishing Decision Boundary Complexity and the Strong First Component

We show that unlike machine learning classifiers, there are no complex boundary structures in the decision boundaries for well-trained deep models. However, we found that the complicated structures do appear in training but they vanish shortly after shaping. This is a pessimistic news if one seeks to capture different levels of complexity in the decision boundary for understanding generalization, which works well in machine learning. Nonetheless, we found that the decision boundaries of predecessor models on the training data are reflective of the final model's generalization. We show how to use the predecessor decision boundaries for studying the generalization of deep models. We have three major findings. One is on the strength of the first principle component of deep models, another about the singularity of optimizers, and the other on the effects of the skip connections in ResNets. Code is at https://github.com/hengshu1/decision_boundary_github.
Which Shortcut Solution Do Question Answering Models Prefer to Learn?

Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples where shortcuts are invalid. Various methods have been proposed to mitigate this problem, but they do not fully take the characteristics of shortcuts themselves into account. We assume that the learnability of shortcuts, i.e., how easy it is to learn a shortcut, is useful to mitigate the problem. Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. We also find that the availability of the preferred shortcuts tends to make the task easier to perform from an information-theoretic viewpoint. Lastly, we experimentally show that the learnability of shortcuts can be utilized to construct an effective QA training set; the more learnable a shortcut is, the smaller the proportion of anti-shortcut examples required to achieve comparable performance on shortcut and anti-shortcut examples. We claim that the learnability of shortcuts should be considered when designing mitigation methods.
SimCS: Simulation for Online Domain-Incremental Continual Segmentation

Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.
QuBOBS, interactive objects and a visual representation to explain quantum computing

We introduce a visual representation of qubits to assist in explaining quantum computing to a broad audience. The representation follows from physical devices that we developed to explain superposition, entanglement, measurement, phases, interference, and quantum gates. We describe how this representation can be used to explain teleportation and the Bennett-Brassard quantum key agreement protocol.
A quantum collisional classifier driven by information reservoir

We investigate the open dynamics of a probe qubit weakly interacting with distinct qubit environments bearing quantum information. We show that the proposed dissipative model yields a binary classification of the reservoir qubits' quantum information in the steady state in the Bloch qubit parameter space, depending on the coupling rates. To describe the dissipation model dynamics, we have adopted the collision model, in which the input information parameters of the reservoir qubits are easily determined. We develop a generalized classification rule based on the results of the micromaser-like master equation where the classification can be described in terms of the Bloch parameters. Moreover, we show that the proposed classification scheme can also be achieved through quantum parameter estimation. Finally, we demonstrate that the proposed dissipative classification scheme is suitable for gradient descent based supervised learning tasks.
Taming Normalizing Flows

We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category. We focus on Normalizing Flows because they can calculate the exact generation probability likelihood for a given image. We demonstrate taming using models that generate human faces, a subdomain with many interesting privacy and bias considerations. Our method can be used in the context of privacy, e.g., removing a specific person from the output of a model, and also in the context of debiasing by forcing a model to output specific image categories according to a given target distribution. Taming is achieved with a fast fine-tuning process without retraining the model from scratch, achieving the goal in a matter of minutes. We evaluate our method qualitatively and quantitatively, showing that the generation quality remains intact, while the desired changes are applied.
DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models

Traditionally, monocular 3D human pose estimation employs a machine learning model to predict the most likely 3D pose for a given input image. However, a single image can be highly ambiguous and induces multiple plausible solutions for the 2D-3D lifting step which results in overly confident 3D pose predictors. To this end, we propose \emph{DiffPose}, a conditional diffusion model, that predicts multiple hypotheses for a given input image. In comparison to similar approaches, our diffusion model is straightforward and avoids intensive hyperparameter tuning, complex network structures, mode collapse, and unstable training. Moreover, we tackle a problem of the common two-step approach that first estimates a distribution of 2D joint locations via joint-wise heatmaps and consecutively approximates them based on first- or second-moment statistics. Since such a simplification of the heatmaps removes valid information about possibly correct, though labeled unlikely, joint locations, we propose to represent the heatmaps as a set of 2D joint candidate samples. To extract information about the original distribution from these samples we introduce our \emph{embedding transformer} that conditions the diffusion model. Experimentally, we show that DiffPose slightly improves upon the state of the art for multi-hypothesis pose estimation for simple poses and outperforms it by a large margin for highly ambiguous poses.
Contextualized Programming Language Documentation

Learning the syntax and semantics of a new programming language is a challenge. It is common for learners to refer to language documentation many times and in many contexts as they build comfort and understanding. We review existing functional language documentation, finding that it tends to be organized according to the structure of the language. Each section interleaves narrative explanations, which introduce precise terminology that is then used consistently, with code examples. Sections often start with simpler special cases of a construct before considering it in full generality.
The Storyteller: Computer Vision Driven Context and Content Generation System

Abstract The human capability of detecting, understanding, and contextualizing objects in the real world by machines has always been a dream for computer scientists. Along with other important and pending challenges in the field of computer vision, image captioning with context and content is an important research problem. In our research, we attempted to develop a human-like storytelling system that can caption images with the perspective of content, context, syntax, and knowledge. Our methodology is a combination of Capsule Networks for image encoding, Knowledge Graph for content and context awareness, and Transformer Neural Networks for decoding. During feature extraction, spatial, geometrical, and orientational details are extracted using Capsule Networks. To equip our model with content, context, and semantics, the corpus is passed through the Knowledge Graph. The decoding phase is a combination of Knowledge Graph and Transformer Neural Network for knowledge-driven captioning. Dynamic multi-headed attention in the decoder is used for memory optimization. Our model is trained over MSCOCO. The results provide good content and context understanding along with B4: 18.23, M: 19.2, R: 41.1, and C: 54.19. The primary outcome of our research is generating autonomous story-type captions for real-world images.
Prediction of Catfish Yield To Fulfill Community Needs Using Multiple Linear Regression Algorithm Method

Fishery is one sector that is important for human life, because most of human needs come from fisheries, one example is the need for catfish. Currently, there is a gap between crop yields and community needs, which is indicated by very high demand while yields are low. Therefore, this study was conducted to predict catfish yields using a linear regression algorithm so that community needs are met. The method used for the training process in this study uses Multiple Linear Regression. Multiple Linear Regression is an analysis conducted on the dependent variable / dependent variable and two or more independent or independent variables. In contrast to simple regression which only has one independent variable and one dependent variable. Predicting the size of the dependent variable using the independent variable data which is already known. The results of this study analyzed the value of Root Mean Squared Error, and Mean Absolute Error. The result of the line equation from the training data for catfish harvesting is yˆ = -310.6119 + 0.0759x1 + 0.0245x2 + 0.6104x3
Developing Android-Digibook with Project-Based Learning Model on Research Method Subject in Guidance and Counselling Department of Medan State University

This study aims to create a digital textbook based on project based Learning Model in research methods Subject. The research using the ADDIE model. The development stage is the stage of manufacture of digibook android-based in accordance with the design plan. Media experts give excellent ratings for throughout the item with the total amount of 82.50 with an average value is 4.10. This application is tested on 30 students of the 1st half of Guidance and Counseling Department of the Faculty of Education Unimed. The results of calculations throughout the student questionnaire responses obtained 87.15%% percentage of aspects of good feelings, 86.57% of the student's interest aspects of the use of the application, 85.85% of aspects of attention, and 88.24% of aspects of student involvement.
The State of Competitiveness in Kuwait Banking Sector

The aim of this study is to examine the competitiveness in the Kuwaiti banking sector for the period 2010 - 2020. The methodology of study assessed the degree of competition among nine national Kuwaiti banks using the Panzar and Rosse model, or the H statistic approach. The study assessed how close the banking sector in Kuwait is to perfect competition. It built a model for the revenues of the banking sector in Kuwait. The model considered various specifications that affect the banking sector’s performance like revenues, risk, deposits, and bank size. In addition, this study analyzed the major factors affecting this sector’s performance. Using the panel data methodology of estimation, the results revealed that the market structure for the banking sector in Kuwait is close to monopolistic competition. Though the banking sector in Kuwait is still in its development stages, the results provided a signal to policy makers regarding their policies. One main implication of the study is to keep strong competition among banks which requires maintaining the stability within the banking system.
Derivations with Holes for Concept-Based Program Synthesis

Program synthesis has the potential to democratize programming by enabling non-programmers to write software. But conventional approaches to synthesis may fail if given insufficient information - a common occurrence when asking non-experts to describe the application they want to write. This paper introduces a new concept-based program synthesis mechanism that can cope with incomplete knowledge, targeting low-code model-driven languages. Concepts are modelled in an ontology that represents user intent including basic actions (e.g. show, filter, and create) along with their associated data as well as basic user interface structures like screens or pages. Our synthesis framework consists of a system of derivation rules that supports deferred premises, which need not be immediately satisfied during synthesis. A derivation in which some deferred premises are missing will thus contain holes; semantically, it represents a proof that is conditional on the developer filling the holes with additional facts from the ontology. We translate derivations with holes to standard first-order logic derivations, where the holes are transformed into assumptions. We illustrate the feasibility and effectiveness of our framework with a proof-of-concept implementation and a set of illustrative examples.
The Application of Picture Media to the Beginning Reading Skills of SD 106 Pongsamelung Students, Luwu Regency

This study aims to describe the beginning reading skills of 106 Pongsamelung Elementary School students. The research method used is descriptive qualitative. The data of this study were students of Elementary School 106 Pongsamelung, using observation, interview and documentation data collection techniques. The data analysis technique uses the Milles and Huberman model with the stages of data reduction, data presentation, and drawing conclusions. The results of this study indicate that early reading learning for grade I students is urgently needed to prepare for the advanced reading stage, because students will have difficulty following and understanding the subject matter if they cannot read, so beginning reading is a student's obligation. Beginning reading learning should be carried out with fun methods and media images for students, so that students do not feel burdened and pressured in learning to read
Flood Control with the Shortcut Method on Drainage Channels in the Kampar District Police Area

Kampar Regency, especially the Polres area, is an area that often experiences stagnant water when it rains with high intensity and long duration. The existing drainage condition is not sufficient to accommodate rainwater runoff. The purpose of this research is to review the hydrological aspects and the capacity of the drainage canals that cause inundation in Kampar Regency, as well as to design and determine the location for the construction of a directional drainage (Short-cut). Rain data used is the last 10 years. The design rain is calculated based on the analysis of the frequency distribution of the log person type III method. The research results show that Qcapacity &gt; Runoff. The design channel length is 190 meters with a 1000x1200 u-ditch drainage type.
The Effect of Smartphone Use on Students' Intensive Reading Achievement

The purpose of this study was to describe the effect of the use of Smartphones on the students’ achievement of Intensive Reading learning in St Augustine Hippo Catholic University. The development of information technology has been so fast, that it has affected every aspect of human life. In addition, the development of information and communication technology has a positive impact on humans but it can also have a negative impact, especially on mobile communication devices (smartphones). The method used in this research is a quantitative research survey design. The research findings indicate several important points, namely: (1) Improvement of students' learning achievement in Intensive Reading, (2) Students are more active in conducting learning activities in class, and (3) The quality of teaching and learning process increases significantly. It can be concluded that the use of a Smartphone could increase students’ achievement.
Optimising for Interpretability: Convolutional Dynamic Alignment Networks

We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly, CoDA Nets can be combined with conventional neural network models to yield powerful classifiers that more easily scale to complex datasets such as Imagenet whilst exhibiting an increased interpretable depth, i.e., the output can be explained well in terms of contributions from intermediate layers within the network.
The Potential of Mangrove Stem Extract (Aegiceras corniculatum) on the Haematocrit Value

Hematocrit examination is an examination used to measure the concentration of red blood cells (erythrocytes) in the blood stated in percent (%). The usual hematocrit examination uses the anticoagulant EDTA, is expensive, and rarely distributed in remote area. Alternative anticoagulants are currently being studied, one of which is the extract of mangrove stems (Aegiceras corniculatum). This study aims to determine the difference in hematocrit values ​​using the anticoagulant EDTA and extract of mangrove stems (Aegiceras corniculatum). The type of this research was analytic observational with a cross-sectional research design. Samples were taken by non-random purposive sampling of 16 female students from a total of 31 students of class D semester VIII of Health Analyst Universitas Muhammadiyah Semarang. The results showed average hematocrit value using EDTA anticoagulant was 42.38% by using the microhematocrit method. Meanwhile, the average hematocrit value using mangrove stem extract (Aegiceras corniculatum) is 20.88%. The hematocrit examination using the EDTA anticoagulant were higher than using the extract of mangrove stems (Aegiceras corniculatum). It showed a value of 0.000 with a significant level of 0.05, which is 0.000 &lt;0.05. Therefore, there is a difference in hematocrit value using EDTA anticoagulant and mangrove stem extract (Aegiceras corniculatum).