Reviewerly/exHarmony huggingface dataset:
Noise analysis and countermeasure for coin processing machine using loudness

Coin processing machine is generally used in financial institutions and stores. This machine automatically counts large number of coins. Hence, the radiated noise at the operational condition sometimes disturbs human activities such as conversation and makes neighboring people uncomfortable. Therefore, it is necessary to identify the main factors increasing the noise and measure them intensively. In this study, we investigated the main process of a coin processing machine generating large noise and the frequency band considering auditory characteristics. Through the loudness analysis using artificial head microphone, the loudness was large when a coin dropped to the turntable, stacker, and withdraw tray. In addition, the sound pressure level (SPL) at 2 ~ 4 kHz was found to increase the loudness. Through further analyses, the SPL at the target frequency band was clarified to be increased by head transfer function and the vibration characteristic of the parts. Based on these results, we conducted countermeasure to the withdrawal tray as an instance, which generated loud noise. As the result, the loudness at the target frequency band could be decreased about 55 % by inserting a soft material to the tray.
One Draw Attack: Fool Deep Learning Models With Modifying Fewer Pixels

Abstract Deep learning plays the leading role in the field of image recognition, natural language processing, and speech processing. But many researches reveal that the existence of adversarial examples would make models vulnerable. Most of the researches focus on restricting norm distance which result in perturbing images too much. With examples given in this paper, we demonstrate that restricting the number of modified pixels is easier to classify in human vision instead of norm distance, meanwhile, it can get a high accuracy of non-targeted attack on VGG16, ResNet50 and DenseNet201. Our method is adding Bézier curves on original image using differential evolution algorithm (DE). Through continuous evolution, kept the good individuals and eliminated the bad ones, DE can guide the search to the best solution. With DE, we do not need to know the gradient information about models, therefore, our method is robust on different models. When we add more Bézier curves on original image, we can get a higher accuracy which also shows that our method is effective. We find that the distribution of prediction labels focuses on a few categories, which means some labels are easier to attack than the most others. We also think the encode order about labels has an influence on the distribution of misclassified labels.
ANALYSIS OF THE NEED FOR INTERACTIVE LEARNING MEDIA BASED ON MOBILE LEARNING IN VOCATIONAL HIGH SCHOOLS

The learning process is not just the delivery of teaching material or receiving teaching material, but there needs to be a clear concept so that the material presented can be received clearly.Technological advances have led to many changes, one of which is in the world of education which is currently giving birth to the concept of media learning.The existence of online learning media will be effective and efficient, because learning is not only during meetings or face-to-face between teachers and participants but can be done whenever and wherever students are.The rapid development of science and technology can be done using the Android operating system.The use of media in learning can assist teachers in delivering subject matter and within the limitations of class hours.Learning media can be designed in accordance with current technological developments.One of the learning media using ICT assistance is in the form of learning media that is operated or based on a smartphone device with the Android operating system.At this time the Android operating system is one of the most popular operating systems and is widely used by the public.
LibGuides: Keep Calm and Study On: Staying Busy

LibGuide serves as a platform to support Butler University students, faculty and staff during remote learning through a variety of resources to engage students academically and socially.
ISIC 2021 front matter

ISIC 2021 has attracted 4 top-shot researchers as advisory The conference committee has taken due care in finalizing the three keynote speakers and the eight invited speakers for the conference They are diversified across the whole world and are eminent experts in their field The conference has also tied up with 14 special sessions with overall 34 chairs There are 21 members as chairs in the main conference organization and approximately 200 technical program committee members from various countries all around the world ISIC 2021 showcases one workshop three tutorials The conference depicts a high geographic diversity with members from around 40 different countries and high gender diversity with more than 35% women in its organization ISIC 2021 is Hybrid (Face-to-face and Online) in mode Though the COVID-19 pandemic hits this initiative to some extent, we have brought out a quality proceeding The review and selection process has ensured that only high quality manuscripts in the area of the conference are accepted for final publication We are glad to share that we received a total number of 110 submissions in the main conference There were 15 desk rejections by the editors Rest 95 were passed to reviewers We invited reviewers to bid for submissions We have managed to get three to six reviews for each submission;with each reviewer getting a maximum of four submissions to review All reviews are from internationally renowned experts Fifty five papers were accepted and finally forty seven papers are published in the proceedings The accepted submissions are spread across 17 different countries with not more than 2-3 papers from any same organization © 2020 Copyright for this paper by its authors
A Capsule Conditional Generative Adversarial Network

Generative Adversarial Networks (GAN) is currently one of the most popular research topics in machine learning. It mainly uses two competing networks, a generator network and a discriminator network to learn from the distribution of real samples. The complexity of the real sample distribution leads to many problems in the stability of the generative adversarial network in the training process. The Deep Convolutional Generative Adversarial Networks (DCGAN) were introduced to solve some problem. The performance quality of the results has been significantly improved by DCGAN, but it still sometimes fails to produce realistic data.Due to the deficiencies above DCGAN, this paper involves the capsule network structure into the conditional generation adversarial network, and proposes the CapsuleCGAN (Capsule Conditional Generative Adverserial Networks) architecture. Through the experiment on the MNIST dataset, comparing CGAN and DCGAN, the results show that the proposed CapsuleCGAN architecture can be successfully trained. From the experimental results, not only the high-quality images can be generated, but also in generating maps as diversity. Finally, we put some forward suggestions and ideas for improvement in the future work.
Deep neural network for handcrafted cost-based multi-view stereo

In the last decades, depth estimation from multi-view is treated as an ill-posed problem. This problem becomes severe with limited data such as sparse-view cases. However, with the presence of Convolutional Neural Network (CNN), recent learning-based depth estimation methods prove effectiveness on occluded and texture-less area where prior works still suffer on handling such issues. They utilize features from CNN layer for constructing cost volume and regress input volume with regression network. To overcome those concerns, we introduce a unique approach by combining hand-crafted and learning-based strategies. Specifically, we utilize the Normalized Cross-Correlation (NCC) cost volume, which is more robust to noise than simple L1 and L2 costs, to improve the photo-consistency between local patches. The entire construction pipeline is implemented by pyOpenCL to speed up the processing time. Finally, we employ the network that estimates depth by regressing handcrafted cost-based plane sweeping volume.
Prospects and risks of using software platforms in the realization of distance learninig in universities

the article deals with the problems of an urgent transition to distance learning in the context of the spread of a new coronavirus infection, provides test results that reflect the main problems and advantages when working with the Moodle learning management system, make conclusions on the prospects for using distance learning aids.
Structural Analyses of the Lunavada–Santrampur Area (Gujarat, India) Using Remote Sensing Images

The Lunavada Group of rocks occupies parts of Sabarkantha and Panchmahal districts in Gujarat (India) and have undergone polyphase deformation in the Kadana Formation at NE Gujarat. This study analyzes folds and lineaments to visualize the tectonics using remote sensing images of Santrampur and the surrounding areas. The DEM data was obtained from ASTER and satellite images from Sentinel-2. Analytical technique such as Topographic openness is used to visualize the openness and closeness of the topography. Various hill shades were generated to understand the area from different sun angles, and the best two directions were merged to interpret the lineaments and were latter plotted on a rose diagram. The study uses Google Earth for identifying various fold geometries. With this chapter, we draw attention to structural geologists, the area Santrampur as an excellent place for fieldwork, training and tectonic research.
Video denoising by BM3D technique with an improved cube construction and SURE shrinkage techniques

This paper attempts to improve denoising efficiency of BM3D technique for videos, i.e., VBM3D. VBM3D constructs 3D cubes from target video frames by a block matching algorithm that minimizes the residual matching error. However, such a cube formation results in sacrificing the pixel correlation in the temporal direction. This paper thus modifies this step to preserve the sub-pixel alignment, which makes the Fourier coefficients of each cube located on a vicinity of a certain plane in the 3-D Fourier domain. Then, SURE-shrinkage technique is separately applied to the inside and outside of the vicinity of the plane to denoise each cube. The experimental results given in this paper demonstrate the validity of our approach.
Spatial distribution of artisanal goldmining in Ghana: Using machine learning and Google Earth Engine to quantify conversion of vegetation to gold mines

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Spatial distribution of artisanal goldmining in Ghana: Using machine learning and Google Earth Engine to quantify conversion of vegetation to gold minesAuthorsAbigailBarenblittiDAmandaPaytoniDDavidLagomasinoTemilolaFatoyinboiDKofiAsareiDKennethAidooHugoPigottCharlesSomOmarSeiduLaurentSmeetsDanielleWoodSee all authors Abigail BarenblittiDCorresponding Author• Submitting AuthorNASA Goddard Space Flight CenteriDhttps://orcid.org/0000-0002-9758-2670view email addressThe email was not providedcopy email addressAmanda PaytoniDNASA Goddard Space Flight CenteriDhttps://orcid.org/0000-0002-6326-1895view email addressThe email was not providedcopy email addressDavid LagomasinoEast Carolina Universityview email addressThe email was not providedcopy email addressTemilola FatoyinboiDNASA GSFCiDhttps://orcid.org/0000-0002-1130-6748view email addressThe email was not providedcopy email addressKofi AsareiDGhana Space Science and Technology InstituteiDhttps://orcid.org/0000-0001-8838-3410view email addressThe email was not providedcopy email addressKenneth AidooGhana Atomic Energy Commissionview email addressThe email was not providedcopy email addressHugo PigottGhana Statistical Serviceview email addressThe email was not providedcopy email addressCharles SomGhana Statistical Serviceview email addressThe email was not providedcopy email addressOmar SeiduGhana Statistical Serviceview email addressThe email was not providedcopy email addressLaurent SmeetsGhana Statistical Serviceview email addressThe email was not providedcopy email addressDanielle WoodMassachusetts Institute of Technologyview email addressThe email was not providedcopy email address
QoE-aware Video Streaming in Heterogeneous Cellular Networks

In the past few years, video streaming over cellular networks has become increasingly popular due to which managing end-user Quality of Experience (QoE) has become a crucial problem. We consider the problem of variable bit rate (VBR) video streaming over heterogeneous cellular networks (HetNets). We address this problem as a joint assignment and scheduling problem and design QoE-aware greedy algorithms to solve it. The greedy assignment algorithm links users to femtocells for downloading the videos from, and the greedy scheduling algorithm fairly allocates time slots to the users assigned to a specific femtocell. Our experiments on real-world 4G LTE wireless connectivity and VBR video traces show the superior performance of our algorithm.
WebSmell: An Efficient Malicious HTTP Traffic Detection Framework Using Data Augmentation

With the increasing complexity of cyberspace infrastructure and its applications, cyberattack is becoming ubiquitous and evolving rapidly. As one of the basic techniques to cyberattack awareness, network traffic anomaly detection has been facing diverse challenges such as low detection ability, huge cost of training data collection, weak generalization of classification model. In this paper, we present WebSmell, a framework that conducts malicious HTTP traffic detection using deep learning with data augmentation based on keywords library avoidance. The proposed method can improve the cross-dataset detection ability, reduce the input cost of training dataset, and make deep learning model have strong generalization even with a small training dataset.
Analysis of a Hydrofoil Craft with a Suspension System

In this paper we investigate the efficacy of augmenting, or replacing, an active height control system for a submerged hydrofoil with a passive system based on springs and dampers. A state-space model for submerged hydrofoils is formulated and extended to allow for a suspension at the front wing, aft wing or both wings. The model is partially verified by obtaining results in the fixed-wing limit and comparing these with experimental data from the MARIN Foiling Future Demonstrator. In the current study we limit ourselves to translational springs, only allowing suspension motion in the heave direction. This results in unfavorable behavior: either the motions increased or the system becomes unstable. It is therefore recommended for future research to try rotational springs.
The Invertible U-Net for Optical-Flow-free Video Interframe Generation.

Video frame interpolation is the task of creating an interface between twoadjacent frames along the time axis. So, instead of simply averaging twoadjacent frames to create an intermediate image, this operation should maintainsemantic continuity with the adjacent frames. Most conventional methods useoptical flow, and various tools such as occlusion handling and object smoothingare indispensable. Since the use of these various tools leads to complexproblems, we tried to tackle the video interframe generation problem withoutusing problematic optical flow. To enable this, we have tried to use a deepneural network with an invertible structure and developed an invertible U-Netwhich is a modified normalizing flow. In addition, we propose a learning methodwith a new consistency loss in the latent space to maintain semantic temporalconsistency between frames. The resolution of the generated image is guaranteedto be identical to that of the original images by using an invertible network.Furthermore, as it is not a random image like the ones by generative models,our network guarantees stable outputs without flicker. Through experiments, weconfirmed the feasibility of the proposed algorithm and would like to suggestinvertible U-Net as a new possibility for baseline in video frameinterpolation. This paper is meaningful in that it is the worlds first attemptto use invertible networks instead of optical flows for video interpolation.
Cacophonic choir

Cacophonic Choir is an interactive sound installation aimed at bringing attention to the firsthand stories of sexual assault survivors and the way such stories may be distorted by the media and in online discourse. The work is composed of nine vocalizing physical agents distributed in space. Each agent tells a story. Altogether, from a distance, the viewer hears an unintelligible choir---the stories are fragmented and the voices distorted. As the viewer approaches an agent, the story becomes sonically and semantically more coherent. When in the agent's personal space, the viewer can hear the firsthand account [1] of a sexual assault survivor. The work employs several digital media techniques, including machine learning, physical computing, digital audio signal processing, and digital design and fabrication. Agents are fitted with ultrasonic sensors and respond to a viewer approaching it in three ways simultaneously. First, the narrative becomes more coherent, reflecting how stories become distorted by the media. This is achieved by adjusting the accuracy of a generative machine learning algorithm that we designed and trained on the anonymous accounts of more than 500 sexual assault survivors. Second, to express how survivors are silenced, the voices are treated by a granular synthesis algorithm that generates a stuttering and halting effect that decreases as the viewer approaches the agent. Third, the individual form of each agent becomes revealed as the result of it illuminating itself from within, enabling the viewer to see through the soft silicon shell to the digitally fabricated organic form inside. Via these interactions, the work embodies the stories of sexual assault survivors and how these stories are obscured and distorted in online public discourse.
Authentication and Tamper Localization of scanned Bill and Seal based on Block Correlation

For important financial documents such as financial statements, financial contracts, bills, accounts, etc., even if there is a very small change in the transmission process, it may lead to huge economic losses, so it is of great significance to verify the content. In addition, the seal symbolizes an undeniable identity, so it should be absolutely credible. In this paper, we propose a method of passive authentication based on the block correlation to distinguish whether or not the documents have been tampered with semantics in the process of circulation after the paper documents are imaged, and then locate the tampered area. Through a large number of experiments, the results show that the tamper detection accuracy and location accuracy of the bill image are up to 96% and 92% respectively; the tamper detection accuracy and location accuracy of the seal are up to 93%) and 88% respectively.
Conceptual Modeling as a Basis for Forming of Creative Skills

Abstract Modern architecture is based on new concept, new methods, requires new approaches, directions and goals, which should not be confined within narrow formal compositional requirements, but should be considered comprehensively: information, science and the environment in general predetermined the development of new directions. On the one hand, the main principles of architectural systems are dynamism, variability, adaptation of the compositional structure to the modern requirements of life, on the other hand, the gradual penetration of new ideas from the field of philosophy, ecology, and anthropology into the field of architecture. This paper is devoted to the formation of creative skills in teaching the basics of architectural design. The main goal of the conceptual approach in student course design is the development of creative potential, the formation of an individual creative method for each student, the importance of understanding the place of architecture in all spheres of human activity and in the overall ecosystem.
Research Guides: Evaluate Sources: Specific Strategies

With so much information coming at us through so many different channels at every increasing speed, how to we make sure that we base our decisions - and enjoyment - on reliable information? How do I evaluate different types of sources
The Farsund Shear Zone: geochemical evidence for lithological diversity in the wall rock of the Rogaland Anorthosite Province, South Norway

The Rogaland Anorthosite Province (RAP) -a typical Proterozoic Anorthosite-Mangerite-Charnockite (AMC) plutonic complex exposed in the Sveconorwegian orogen in South Norway -was emplaced diapirically through the crust, along a shear zone, at c. 933-916 Ma.The shear zone, recently defined as the Farsund Shear Zone (FSZ), crops out along the eastern flank of the anorthosite province, and it is made up of strongly foliated, steeply dipping, banded gneisses.The banded gneisses comprise a diversity of lithologies: metabasites, granitoid gneisses, augen gneisses and kinzigitic gneisses.Major and trace element compositions of samples mostly from the banded gneisses and the neighbouring granite gneiss permit us to unravel the nature of the various protoliths.The kinzigitic gneisses result from metamorphism of pelitic sediments, while the augen gneisses belong to the high-K calc-alkaline series similar to the Feda suite (c.1050 Ma), a major component of the Sirdal magmatic belt (SMB, 1070-1020 Ma).The metabasites comprise jotunites comparable to the intermediate rocks of the AMC suite (933-916 Ma) as well as amphibolites and norites with trace element signatures consistent with an oceanic origin.Charnockitic gneisses could result from anatexis in CO 2 -rich conditions or from fractionation of felsic magma similar to that of the charnockite-grante Farsund intrusion (931-926 Ma).Some leucogranitic layers have a typical REE distribution of migmatitic leucosome.Other granite layers can be distinguished from the massive granite gneiss that has higher Th, Pb and Rb concentrations and [La/Yb] N ratios, but all granites display high-K calc-alkaline affinities compatible with an island-arc origin.The granite gneiss is comparable to the SMB rocks and the granite layers to the Suldal arc lithologies (c.1520-1480 Ma).The FSZ hosts thin interleaved layers of rocks corresponding to the major lithologies exposed at regional scale.This includes jotunites and charnockites probably genetically related to intrusion of the c. 930 Ma anorthosite province, and a variety of granitic gneisses, metasedimentary rocks, augen gneisses, and mafic rocks with protolith ages ranging from c. 1020 Ma to c. 1500 Ma.
Bayesian Network Algorithms Used in Assessment of Learners' Learning Behaviour

In order to solve the problem that teachers cannot monitor learners' learning behaviour or evaluate learners' learning objectively and scientifically because of the relative separation of time and space between teachers and students, covering Bayesian network learning evaluation model based on knowledge relationship is proposed. The information related to students can be divided into two parts: domain-related information and domain-independent information. Firstly, the modelling of domain-related information is mainly discussed. The process of domain-related information modelling is the Bayesian networking process of the courses that students have learned. Secondly, the time and space complexity of the algorithm is reduced by simplifying the network structure and optimising the order of node deletion in the triangulation process. The results show that the model can accurately judge the degree of students' mastery of knowledge, and can provide students with personalised guidance strategy.
COVID-19: Emergency Appeal for Journalism and Media Support

On this World Press Freedom Day, the undersigned organisations honour those who work tirelessly to help keep the public informed and call for robust ...