Reviewerly/exHarmony huggingface dataset:
Dense depth estimation from multiple 360-degree images using virtual depth

In this paper, we propose a dense depth estimation pipeline for multiview 360∘ images. The proposed pipeline leverages a spherical camera model that compensates for radial distortion in 360∘ images. The key contribution of this paper is the extension of a spherical camera model to multiview by introducing a translation scaling scheme. Moreover, we propose an effective dense depth estimation method by setting virtual depth and minimizing photonic reprojection error. We validate the performance of the proposed pipeline using the images of natural scenes as well as the synthesized dataset for quantitive evaluation. The experimental results verify that the proposed pipeline improves estimation accuracy compared to the current state-of-art dense depth estimation methods.
Probabilistic description of short-term cloud dynamics from rapid sampling of the solar spectral irradiance

Solar irradiance variability due to stochastic cloud dynamics can cause unwanted fluctuations in the output voltage of photovoltaic (PV) modules. These dynamics must in particular be understood at very-short and short time scales if grid interconnection and generation/load balance requirements are to be maintained for PV distributed across the grid edge. Using a recently-created database for Ottawa, Canada, a 6-month longitudinal study was conducted with a specific focus on cloud dynamics. A spectral pyranometer was used to derive full-range spectral and broadband global horizontal irradiance under all sky conditions every 250 ms. Exploiting the infrared (IR) measurement channel of this software-augmented multi-filter radiometer allowed the cloud dynamics to be probed across time scales ranging from the subsecond to minutes. Seven distinct sky conditions were self-consistently determined without sky imaging. Probability distributions, established via kernel density estimates (KDE), allowed the statistical dependence of these conditions on the spectral clear-sky index to be found. The stochastic nature of the spectral irradiance variability was probed using spectral clear-sky index increments, over time steps that were found to span three distinct variability regimes.
Deep learning-based stereo matching using the feature spatial pyramid pooling

For deep learning-based stereo matching, despite deep convolution networks possessing rich, high-level semantic features that are usually abstract and low-resolution, it is difficult to accurately calculate per-pixel correspondence without image detail information in challenging regions, such as thin objects, edge contours, reflective surfaces, etc. Motivated by the information propagation capability of the feature pyramid network (FPN), we focus on using FPN to improve the detailed comprehension of spatial pyramid pooling (SPP). We propose a feature SPP (FSPP), which consists of FPN and SPP. FSPP integrates high-frequency details reserved by FPN into multiscale features extracted by SPP to form a feature volume considering both detailed and contextual information. Using FSPP, features become distinct and alleviate the ambiguity of matching progress in challenging regions. Furthermore, we concatenate FSPP to Pyramid Stereo Matching Network with three-dimensional convolution and hourglass architecture. We also evaluate its effectiveness on the KITTI 2012/2015 and the Middlebury 2014/2021 datasets. The results show that the proposed algorithm performs better than current state-of-the-art algorithms in challenging regions while taking on better generalization.
Class-GE2E: Speaker Verification Using Self-Attention and Transfer Learning with Loss Combination

Recent studies prove that speaker verification performance improves by employing an attention mechanism compared to using temporal and statistical pooling techniques. This paper proposes an advanced multi-head attention method, which utilizes a sorted vector of the frame-level features to consider a higher correlation. In this study, we also propose a transfer learning scheme to maximize the effectiveness of the two loss functions, which are the classifier-based cross entropy loss function and metric-based GE2E loss function, to learn the distance between embeddings. The sorted multi-head attention (SMHA) method outperforms the conventional attention methods showing 4.55% in equal error rate (EER). The proposed transfer learning scheme with Class-GE2E loss function significantly improved our attention-based systems. In particular, the EER of the SMHA decreased to 4.39% by employing transfer learning with Class-GE2E loss. The experimental results demonstrate that our effort to include a greater correlation between frame-level features for multi-head attention processing, and the combining of two different loss functions through transfer learning, is highly effective for improving speaker verification performance.
Application to combinatorial optimization problems

Abstract This Chapter discusses a wide variety of combinatorial optimization problems where the numerical solution of the ordinary differential equations of DMMs shows advantages over traditional algorithms.
Human activity recognition with smartphones using machine learning process

Identifying human exercises requires foreseeing human exercises dependent on sensor information. Lately, it has become known for its numerous product applications that utilization current PC equipment. It records data on exercises like strolling, climbing steps, venturing down, sitting, standing, and resting. Sensor detecting was performed utilizing a speedometer and a spinner, and a sensor (speed and whirligig) sensor was recently performed utilizing a commotion channel. Sensor speed signals incorporate weight and body parts that are isolated by body speed and weight utilizing a little Butterworth channel. The power of gravity is viewed as just a little division. Property boundaries have been found in computing changes over and over once more. The objective is to precisely foresee machine-based procedures to decide human movement. Information investigation utilizing SMLT, change location, single direction examination, two-layered and multifaceted investigation, negation, information approval, information cleaning/planning, and information investigation. Show all entries. Give machine-based strategies to precisely foresee the worth of the cost by contrasting AI calculations with precisely decide the ascent in stock costs or the condition of the economy. What&#x0027;s more, think about the exhibition of various AI calculations from the car office chronicles and examine and assess them. Discover the data gave along the disarray framework and the classification appraisal report and afterward put the data in the main class, and the outcomes show that the exhibition of the proposed calculation AI calculation can be precisely contrasted with the real, update, and F1 scores.
A Multioffspring Genetic Algorithm Based on Sorting Grouping Selection and Combination Pairing Crossover

A multi-offspring genetic algorithm based on sorting grouping selection and combination pairing crossover was proposed in this paper. First, individuals in the population were sorted according to their objective function values, and the remaining individuals were divided into several groups after eliminating the worst. Then, the selection operation, which has the advantage of a simplified calculation that can be easily performed, was implemented. Second, a combination pairing crossover operator was designed. Individuals from different groups were selected for new combinations. In a combination, if a crossover condition was met, individuals pairing and transposons crossover operation were made. Otherwise, offspring copies of the individuals in the combination were used, which increase the opportunity of generating better offspring by producing multi-offspring. Finally, a new population was formed by adopting basic bit mutation operator, elitism policy and the strategy of multi-offspring population competition. Moreover, the catastrophe mechanism has been introduced into improved algorithm to avoid premature convergence. The test results on the functions of CEC 2017 test suites shown that the algorithm proposed in this paper has better search performance, stability and faster convergence to the global optimal solution. These results thus verified the effectiveness and feasibility of the algorithm proposed in this paper. Applying the improved algorithm to the location optimization problem of agricultural product logistics facilities, it shown that the improved algorithm is an effective method to solve the location optimization.
Advertisement replacement in video

Advertising in TV shows and movies is expensive and has one of the largest market shares in the entire advertising industry. We address the task of adding a given advertising banner in a given video. In this paper, we propose a new algorithm for processing and replacing advertising banners in videos, which preserves the quality of the original video content. This algorithm allows the given posters to be inserted into a video in a fully automated mode. In order to replace a banner, the algorithm requires only a video and an image of the banner to be inserted. Our algorithm uses computer vision methods for localizing banners on the scene, analyzing, and transforming them. We suggest the approach to create a synthetic dataset for fine-tuning advertising banners detection models. We implement three various methods for the banner localization task and compared the approaches with each other and existing methods. The source code and examples of the algorithm performance are publicly available https://github.com/leonfed/ReAds.
Active constraints selection based on density peak

Semi-supervised clustering which aims to integrate side information to improve the performance of clustering process, has received a lot of attentions in research community. Generally, there are two kinds of side information called seed (labelled data) and constraint (must-link, cannot-link). By integrating information provided by the user or domain expert, the semi-supervised clustering can produce expected results of users. In fact, clustering results usually depend on side information provided, so different side information will produce different results. In some cases, the performance of clustering may decrease if the side information is not carefully chosen. This paper addresses the problem of selecting good constraints for semi-supervised clustering algorithms. For this purpose, we propose an active learning algorithm for the constraints collection task, which relies on the min-max algorithm and peaks estimation based on density score. Experiments conducted on some real data sets from UCI show the effectiveness of our approach.
Media Literacy and Sourcing Skills for Middle Grades Students

People of all ages have difficulty interpreting the information they read online. The contemporary media environment grants the public access to an unprecedented amount of information, and virtual learning during the COVID-19 pandemic has necessitated a better understanding of that technology and students’ utilization of such tools. It is widely known that students of today have access to a wealth of information on the internet, but a virtual school year has revealed that 1) students at home using internet searches for school will often select the first answer they find when searching for information and 2) students generally believe what they read on the internet and social media to be true, suggesting a need in secondary education for teaching media literacy and sourcing skills. This article will examine this necessity of teaching these skills in Health and Physical Education, starting in middle grades.
A Test of Free Speech: Applying the Ethics of Care to Coverage of<i>Snyder V. Phelps</i>

U.S. journalists must walk a fine line when reporting on hate speech. Journalists have a vested interest in standing up for the First Amendment, which gives them the freedom to do their work. However, the legal protection that people who spew hateful rhetoric enjoy vastly outweighs any protections upon which the victims can rely. As such, dealing with hate speech in the United States is an inherently ethical issue. Applying the ethics of care to their reporting would allow journalists a clear framework with which to counter hate speech. This study examines if and how journalists used the ethics of care framework when covering the Snyder v. Phelps Supreme Court case through analysis of articles published in U.S. newspapers. Using these articles as a representative sample for the national coverage of the case, the study finds that journalists failed to consider the human impact of their reporting.
Design of Cooling System of Tier 3 Standard for Rack Enclosure in the Containerized Data Centers

The purpose study was to illustrate how the function of the designed tier 3 standard for rack enclosure in the containerized data centers (Containerized DC) with Thailand data center standards. Our results of the design show that containerized data centers achieve 95% and 60% savings in the cooling system in containers and facility powers respectively compared to a building data center and that savings of 60% in cooling power are possible when workloads are consolidated onto the least number of containers. We also show that the new cooling system optimizations technique is very effective at high utilization and that this data center can approach the efficiency of a container at high utilizations when employing a simple cooling optimization.
A cross-lingual video classification using subtitles

The amount of multimedia content is being proliferated day by day, making the production of searchable video archives and developing information retrieval engines very challenging. Therefore, automatic video classification has become a critical requirement for different fields, especially information retrieval. Video topic classification is among the essential steps toward multimedia content understanding. However, this classification primarily requires an analysis of the video content. In this paper, we propose an automatic categorization of TEDx talks using subtitles of videos. To this aim, we implemented a supervised machine learning SVM-based classifier. The classifier was first trained on multilingual parallel subtitles in English, Arabic, and Brazilian Portuguese. Then, a voting method is used to combine the classification results. Besides, preprocessing techniques were included along with the TF-IDF and Chi-Square as feature extraction and selection methods. The overall performance achieved by our proposed approach is 0.92, which is among the highest accuracy rates published over similar studies.
Optimizing 3D U-Net-based Brain Tumor Segmentation with Integer-arithmetic Deep Learning Accelerators

While gliomas have become the most common cancerous brain tumors, manual diagnoses from 3D MRIs are time-consuming and possibly inconsistent when conducted by different radiotherapists, which leads to the pressing demand for automatic segmentation of brain tumors. State-of-the-art approaches employ FCNs to automatically segment the MRI scans. In particular, 3D U-Net has achieved notable performance and motivated a series of subsequent works. However, their significant size and heavy computation have impeded their actual deployment. Although there exists a body of literature on the compression of CNNs using low-precision representations, they either focus on storage reduction without computational improvement or cause severe performance degradation. In this article, we propose a CNN training algorithm that approximates weights and activations using non-negative integers along with trained affine mapping functions. Moreover, our approach allows the dot-product operations to be performed in an integer-arithmetic manner and defers the floating-point decoding and encoding phases until the end of layers. Experimental results on BraTS 2018 show that our trained affine mapping approach achieves near full-precision dice accuracy with 8-bit weights and activations. In addition, we achieve a dice accuracy within 0.005 and 0.01 of the full-precision counterparts when using 4-bit and 2-bit precisions, respectively.
Image retargeting quality assessment based on saliency-driven classification

The overall geometric distortions of the visually prominent contents in resized images are sensitive to human perception. But the existing image retargeting quality assessment (IRQA) algorithms did not consider it, leading to a lower performance of evaluation algorithms. Additionally, the visually important region needs to be extracted from the retargeted images to measure their distortions. However, due to the size change and content alternation during retargeting, the visually prominent regions of the resized images obtained by the existing saliency detection approaches do not match the corresponding portions of the original photos. To address these issues, we propose an IRQA method based on saliency-driven classification, which uses various metrics to evaluate the qualities of different retargeted images. We also design a novel algorithm to extract the salient region of the retargeted image, which utilizes the original image to perform backward registration and position matching of the salient area. Besides, we present an improved aspect ratio similarity (ARS) metric to measure the overall geometric distortion occurring in the visually important region of the resized images. Experimental results show that the performance of the proposed IRQA method is superior to the other existing algorithms. • We propose a novel salient region extraction method for retargeted images. • We develop an image classification algorithm using the foreground and gradient maps. • We design a salient region-based ARS metric to measure the geometric distortion.
Note su infiniti flessi in chianino: un approccio cartografico

In this article, we investigate a morpho-syntactic phenomenon in Chianino similar to the inflected infinitive discussed for Romance. Chianino is an Italo-Romance variety spoken in the southeast of the province of Siena. The analysed structures involve pairs of inflected verbs merged together. The candidates for the higher verb of the pair are (i) the root of the modal dové ‘must’ and (ii) the subjunctive form of avé ‘to have’. The second element of the pair can only be a non-suppletive inflected infinitive form of the verb èsse ‘to be’, as in the cases of the second person singular dev-ess-i ‘you must be’ and abbi-ess-i ‘you have to be’ (meaning: ‘you should be’). From a pragmatic point of view, the double inflected forms avé + èsse can only have epistemic readings, while dové + èsse can also have a jussive connotation. After having briefly introduced the variety under investigation, we will discuss observational and introspective data. We will then proceed to a formal analysis. Adopting a cartographic approach, we consider the second element of the pair as a head located in a dedicated functional projection within the syntactic architecture.
Surface shape detection of asymmetric double-sided off-axis aspheric lens based on spatial light modulator

With the improvement of optical lens manufacturing and processing capabilities, the surface shape of optical components is becoming more and more complex, such as asymmetric double-sided off-axis aspheric lenses. Generally, people use two different surface detection equipment to detect this type of optical surface, which is inefficient and not universal. Thus in this paper, we present a dull Compensation method based on Spatial Light Modulator. In this method, we detected an asymmetric double-sided off-axis aspheric mirror with convex and concave surfaces with just one detection equipment. For the concave surface, the residual wave aberration is 0.0002&lambda; (Peak to Valley), for the convex surface, the residual wave aberration is 0.0097&lambda; (Peak to Valley). The result shows that this method is feasible in testing asymmetric double-sided off-axis aspheric lenses.
SOSP: A SmartNIC-based Offloading Framework for Cloud Storage Pooling

As Moore's Law is gradually reaching its limitation, traditional CPU-centric computing architecture cannot meet the ever growing computational requirements, especially in large distributed data centers. There is a growing consensus in the industry that the future architecture is a data-centric fabric which can better integrate the function of computation, storage and network. In order to reduce the pressure on CPUs in data centers and further facilitate the transformation of the entire architecture towards higher flexibility and efficiency, this paper presents a SmartNIC-based offloading framework for storage pooling (SOSP) by leveraging the functionality of SmartNICs to offload both the local and remote storage services from CPUs. Our experiments show that the SmartNIC-based solution SOSP can increase the I/O random Read/Write operation performance by around 20-25% and 13-15% respectively, and at the meantime, free up host CPU cores by saving 16.7% virtual cores compared to the solution without using SmartNICs.
Augmented Reality as a Didactic Resource: A Proposal for Computer Equipment Maintenance Training

The didactic resources are mediators for the development and growth of the teaching-learning process, they support the interaction between the teacher, the student and the object of knowledge. Information technologies have come to transform education, through rapid access to information, and multiple sources of knowledge, facilitating communication, creativity, and innovation. One of the technologies that currently has the greatest momentum is extended reality, (XR), the augmented reality is one of its elements, based on the observation of the real world augmented with additional information generated by a computer, and with interaction that makes up the synthetic with images obtained from a real world. The objective of this work is to develop a didactic resource based on augmented reality techniques that makes it easier for technical high school students to learn the maintenance and configuration of computer equipment. This work was evaluated by means of an instrument that contemplates 3 dimensions: usability, design and satisfaction applying to students from the northeast of Mexico area, obtaining the following results, usability was evaluated by the students with 95.9%, design with 97.8% and satisfaction with 98.6%. It is concluded that augmented reality is an emerging technology that can be integrated into the planning as a didactic resource according to the context of the user and in case of social isolation supports us to give continuity to the educational process, its development should be with a multi-disciplinary approach which would allow obtaining a design where elements that recreate the teaching process - learning are integrated.