Reviewerly/exHarmony huggingface dataset:
Baking Neural Radiance Fields for Real-Time View Synthesis

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints. However, NeRF's computational requirements are prohibitive for real-time applications: rendering views from a trained NeRF requires querying a multilayer perceptron (MLP) hundreds of times per ray. We present a method to train a NeRF, then precompute and store (i.e. bake) it as a novel representation called a Sparse Neural Radiance Grid (SNeRG) that enables real-time rendering on commodity hardware. To achieve this, we introduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid representation with learned feature vectors. The resulting scene representation retains NeRF's ability to render fine geometric details and view-dependent appearance, is compact (averaging less than 90 MB per scene), and can be rendered in real-time (higher than 30 frames per second on a laptop GPU). Actual screen captures are shown in our video.
Modeling the spread of dangerous pandemics with the utilization of a hybrid-statistical–Advanced-Fuzzy-Cognitive-Map algorithm: the example of COVID-19

The novel Coronavirus SARS-coV-2 outbreak late in 2019 and early 2020, known today as the COVID-19 pandemic, has spread fast throughout the world. It has considerably affected the lives of all people around the globe while the number of deaths related to the pandemic keeps increasing worldwide. Being able to predict the spread of the pandemic has been very helpful to governments to decide on actions. Statistical prediction models are capable of modeling a single snapshot but have several well-known weaknesses, such as linear assumptions between pandemic variables, while they cannot confirm the actual causality between studied factors. In the present work, the authors propose a state space Advanced Fuzzy Cognitive Maps (AFCM) approach model to predict the spread of the pandemic, using dynamic cause and effect relationships between pre-defined factors. State-Space Advanced Fuzzy Cognitive Maps are proposed for modeling the spread of the pandemic, utilizing several social, policy, and healthcare factors. Statistical data from Greece, South Korea, and Germany are gathered to evaluate the performance of the proposed model. The proposed methodology was able to predict the pandemic trend in the studied countries, in terms of the total number of confirmed patient cases, yielding a coefficient of determination of 0.99, 0.94, and 0.97 respectively. The Pearson's correlation coefficient was found to be 0.99, 0.97, and 0.98 respectively. The results demonstrate the effectiveness and the advantages of the proposed methodology when modeling uncertain and dynamic situations, like novel pandemics.
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network

Establishing robust and accurate correspondences between a pair of images is a long-standing computer vision problem with numerous applications. While classically dominated by sparse methods, emerging dense approaches offer a compelling alternative paradigm that avoids the keypoint detection step. However, dense flow estimation is often inaccurate in the case of large displacements, occlusions, or homogeneous regions. In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches. We propose the Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences along with a reliable confidence map. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the tasks of pose estimation, 3D reconstruction, image-based localization, and image retrieval.
Hybrid Aggregation Network for Survival Analysis from Whole Slide Histopathological Images

Understanding of prognosis and mortality is crucial for evaluating the treatment plans for patients. Recent developments of digital pathology and deep learning bring the possibility of predicting survival time using histopathology whole slide images (WSIs). However, most prevalent methods usually rely on a small set of patches sampled from a WSI and are unable to directly learn from an entire WSI. We argue that a small patch set cannot fully represent patients’ survival risks due to the heterogeneity of tumors; moreover, multiple WSIs from one patient need to be evaluated together. In this paper, we propose a Hybrid Aggregation Network (HANet) to adaptively aggregate information from multiple WSIs of one patient for survival analysis. Specifically, we first extract features from WSIs using a convolutional neural network trained in a self-supervised manner, and further aggregate feature maps using two proposed aggregation modules. The self-aggregation module propagates informative features to the entire WSI, and further abstract features to region representations. The WSI-aggregation module fuses all the region representations from different WSIs of one patient to predict patient-level survival risk. We conduct experiments on two WSI datasets that have accompanying survival data, i.e., NLST and TCGA-LUSC. The proposed method achieves state-of-the-art performances with concordance indices of 0.734 for NLST and 0.668 for TCGA-LUSC, outperforming existing approaches.
LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs

Low-precision computation, which has been widely supported in contemporary hardware, is considered as one of the most effective methods to accelerate convolutional neural networks. However, low-precision computation is not widely used to speed up Winograd, an algorithm for fast convolution computation, due to the numerical error introduced by combining Winograd transformation and quantization. In this paper, we propose a low-precision Winograd convolution approach, LoWino, based on post-training quantization, which employs a linear quantization method in the Winograd domain to reduce the precision loss caused by transformations. Moreover, we present an efficient implementation that integrates well-designed optimization techniques, thereby adequately exploiting the capability of low-precision computation on modern CPUs. We evaluate our approach on Intel Xeon Scalable Processors by leveraging representative convolutional layers in prevailing deep neural networks. Experimental results show that LoWino achieves up to 2.04 × speedup over state-of-the-art implementations in the vendor library while maintaining the accuracy at a reasonable level.
Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.
Parametric Contrastive Learning

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones, e.g., our ResNet-200 achieves 81.8% top-1 accuracy. Our code is available at this https URL.
Reasoning about Explanations for Non-validation in SHACL

The Shapes Constraint Language (SHACL) is a recently standardized language for describing and validating constraints over RDF graphs. The SHACL specification describes the so-called validation reports, which are meant to explain to the users the outcome of validating an RDF graph against a collection of constraints. Specifically, explaining the reasons why the input graph does not satisfy the constraints is challenging. In fact, the current SHACL standard leaves it open on how such explanations can be provided to the users. In this paper, inspired by works on logic-based abduction and database repairs, we study the problem of explaining non-validation of SHACL constraints. In particular, in our framework non-validation is explained using the notion of a repair, i.e., a collection of additions and deletions whose application on an input graph results in a repaired graph that does satisfy the given SHACL constraints. We define a collection of decision problems for reasoning about explanations, possibly restricting to explanations that are minimal with respect to cardinality or set inclusion. We provide a detailed characterization of the computational complexity of those reasoning tasks, including the combined and the data complexity.
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker. This approach makes two simplifying assumptions: a) phonemes can be recognized from speech with high accuracy, b) there is a single correct way for a sentence to be pronounced. These assumptions do not always hold, which can result in a significant amount of false mispronunciation alarms. We propose a novel approach to overcome this problem based on two principles: a) taking into account uncertainty in the automatic phoneme recognition step, b) accounting for the fact that there may be multiple valid pronunciations. We evaluate the model on non-native (L2) English speech of German, Italian and Polish speakers, where it is shown to increase the precision of detecting mispronunciations by up to 18% (relative) compared to the common approach.
Computer Network Design for the teaching area of the Elvira Cape library

Interconnected devices have provided companies and individuals with the advantage that information travels from one place to another, making information processes more viable. The interconnection networks are responsible for providing everything necessary for the machine to have an adequate passage of messages and use of commutations. In this document a design of a LAN network is developed for the area of ​​a library which needs to expand its communication network, thus forming a computer network to connect different types of computers sharing information links. These data links are established through means such as cable, optical cables, or wireless means such as WIFI.
A Human in the Loop: AI won't Surpass Human Intelligence Anytime Soon

We are well into the third wave of major investment in artificial intelligence. So it's a fine time to take a historical perspective on the current success of AI. In the 1960s, the early AI researchers often breathlessly predicted that human-level intelligent machines were only 10 years away. That form of AI was based on logical reasoning with symbols, and was carried out with what today seem like ludicrously slow digital computers. Those same researchers considered and rejected neural networks. • In the 1980s, AI's second age was based on two technologies: rule-based expert systems—a more heuristic form of symbol-based logical reasoning—and a resurgence in neural networks triggered by the emergence of new training algorithms. Again, there were breathless predictions about the end of human dominance in intelligence.
High dynamic range 3D measurements based on space–time speckle correlation and color camera

Structured light (SL) based three-dimensional (3D) measurement struggles to estimate high dynamic range (HDR) scenes, where both high and low reflectivity parts exist simultaneously. This paper proposes a method through the joint design and optimization of hardware and algorithms, in which only four frames are required to realize the 3D reconstruction of HDR scenes. The height information of each sub-area in the scene under test can be encoded effectively by temporally projecting two sets of complementary speckle patterns onto target surface. To decode the corresponding patterns captured by the cameras, we design a stereo matching strategy consisting of space-time binary feature (ST-BIF) descriptor preliminary screening and zero-mean normalized cross-correlation (ST-ZNCC) final retrieval. The ST-BIF descriptor based on neighborhood comparison is designed to describe the space-time relative intensity change of projected speckles. Besides the HDR adaptability, the ST-BIF descriptor can effectively improve the matching speed. In addition, the measurable dynamic range can be further improved by fusing all channel disparities as evaluated results, benefitting from the different response of R, G and B channels in color camera to monochromatic light. Experiments are conducted to demonstrate the feasibility of the proposed method. The results indicate that our method achieves the root mean square error 0.2516mm (vs. 1.0668 by commonly used ZNCC) and an average coverage rate up to 94.87% (vs. 93.35% by commonly used ZNCC). Furthermore, the experimental results show that the proposed method can achieve 3D reconstruction of HDR scenes including specular reflection region.
A Rate-Distortion Framework for Explaining Black-Box Model Decisions

Abstract We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations of the target input signal and applies to any differentiable pre-trained model such as neural networks. Our experiments demonstrate the framework’s adaptability to diverse data modalities, particularly images, audio, and physical simulations of urban environments.
Improved quadruple sparse census transform and adaptive multi‐shape aggregation algorithms for precise stereo matching

Stereo matching is an important computer vision tool for three-dimensional (3D) positioning, 3D visual representation, precision detection and recognition with 3D information. For 3D visual representation, for example, the depth image-based rendering (DIBR) needs high accuracy depth maps to synthesise multiple views for 3D naked-eyes displays. It is hard to predict high-precision disparity for smooth regions and occlusion areas by traditional census-based stereo matching methods. In this work, two new improved algorithms, called the improved quadruple sparse census transform and the adaptive multi-shape aggregation, are proposed to achieve a precise stereo matching system. Instead of a binary threshold, the improved quadruple auxiliary census function with an adaptive threshold and the patch mean can raise the discrimination performance for better stereo matching. The selected sparse census patch is proposed to reduce the computation of the pixel-matching cost. For effective aggregation of matching costs, multi-shape windows and a texture-aware algorithm is suggested to decide a suitable window. The proposed adaptive multi-shape aggregation method performs better and solves the discontinuous depth problem occurring near the object boundaries. Experimental results show that the proposed stereo matching system with the proposed quadruple sparse census transform and multi-shape cost aggregation achieves better raw depth maps than the existing algorithms for stereo matching.
Corrective Guidance and Learning for Dialogue Management

Establishing robust dialogue policy with low computation cost is challenging, especially for multi-domain task-oriented dialogue management due to the high complexity in state and action spaces. The previous works mostly using the deterministic policy optimization only attain moderate performance. Meanwhile, state-of-the-art result that uses end-to-end approach is computationally demanding since it utilizes a large-scaled language model based on the generative pre-trained transformer-2 (GPT-2). In this study, a new learning procedure consisting of three learning stages is presented to improve multi-domain dialogue management with corrective guidance. Firstly, the behavior cloning with an auxiliary task is developed to build a robust pre-trained model by mitigating the causal confusion problem in imitation learning. Next, the pre-trained model is rectified by using reinforcement learning via the proximal policy optimization. Lastly, human-in-the-loop learning strategy is fulfilled to enhance the agent performance by directly providing corrective feedback from rule-based agent so that the agent is prevented to trap in confounded states. The experiments on end-to-end evaluation show that the proposed learning method achieves state-of-the-art result by performing nearly identical to the rule-based agent. This method outperforms the second place of 9th dialog system technology challenge (DSTC9) track 2 that uses GPT-2 as the core model in dialogue management.
Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques

This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of the personality traits based on motivation scale and depression, anxiety, and stress scales were measured. A sample of 188 students from the Computer Engineering Schools of the Pontifical Catholic University of Valparaíso was used. Through econometric two-stage least squares and paired sample correlation analysis, the results obtained indicate that there is a relation between academic performance and the personality traits measured by educational motivation scale and the ranking of university entrance and gender. In addition, these results led to characterization of students based on their personality traits and provided elements that may enhance the development of an effective personality that allows students to successfully face their environment, playing an important role in the educational process.
The Development of Interactive E-Book of Local History for Senior High School in Improving Local Wisdom and Digital Literacy

&lt;p style="text-align: justify;"&gt;This study discusses students’ responses and perceptions on the e-book of Local History of West Java (Indonesia) developed by the researchers. It uses a Research and Development approach and experimental method. Data collection techniques used in this study are observation, interviews, and focus group discussions with interactive model data analysis. The research subjects were students and teachers of high schools in West Java. They were taken as the subjects as they can represent the region. The History E-book is, however, published on ebooksejarah.id page while the system was developed by using SDLC (Software Development Life Cycle) Waterfall model. The results of the study indicate that the components of the e-book on the aspects of the materials, presentation, and completeness are overall suitable to be used by students. The materials are considered to be still inapplicable and not contextual. Further, they are still lack of latest issues as well as photos/illustrations. The study of student responses on the e-book reinforces that the zoomers (those belong to Generation Z) prefer to digital learning media because their social life belongs to the online world. Local history e-books should better adapt to the learning style of Generation Z, who prefer visuals and hands-on experiences to reading texts. Thus, e-book development needs to be equipped with various learning media in short audios and video explainers, animations, and infographics.&lt;/p&gt;
A Selected Deep Learning Cancer Prediction Framework

Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many researchers have adopted predictive frameworks such as machine learning and DL to predict cancer prognosis, in addition to the probability of its recurrence, progression, and the patients’ survival estimation. Currently, all stakeholders are interested in the accuracy of cancer prognosis prediction. This study selected a framework within high accuracy and short prediction time from three DL frameworks for improving the performance of cancer prognosis prediction. This prediction requires a quick and high-accuracy optimizer, so we propose a binary version of the continuous AC-parametric whale optimization algorithm. This version is built on S-shaped transfer functions to identify the minimal optimal subset of features and maximize the classification accuracy. These frameworks proposed have the following forms: the first is a Feed-Forward Neural Network (FFNN) in which the input is the optimal set of feature selection. The second is an optimized parameter FFNN. The third is composed of a feature selection layer in which the best subset of selected features is for use as inputs in the optimized FFNN. We compared these frameworks using a comparative study. Our results show that, under all conditions, the third framework is superior to the others with an average accuracy of 100%, whereas the first and second frameworks achieved 94.97% and 93.12% accuracy, respectively.