Reviewerly/exHarmony huggingface dataset:
Differentiable Rendering for Pose Estimation in Proximity Operations

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.
An Image Denoising Method for Real Scene Based on Pixel-Level Noise Estimation

The majority of image denoising algorithms assume that the noise is evenly distributed white Gaussian noise, however, the image noise which collected in real scenes is more complex. In this paper, we propose a real image denoising method based on pixel-level noise estimation. The method is improved on the basis of the block-matching and 3D filtering (BM3D) image denoising algorithm. The noise estimation algorithm further introduces pixel-level non-local self similarity (NSS) on the basis of patch-level NSS prior. After detecting the flatness of the image block, the relevant parameters are adaptively adjusted, finally the noise estimation algorithm and the image denoising algorithm are combined block by block. Experiments show that this noise estimation method greatly reduce the required processing time while ensuring the accuracy of noise estimation. The image denoising effect has certain superiority compared with other classic traditional denoising algorithms.
Active Label Correction Using Robust Parameter Update and Entropy Propagation

Label noise is prevalent in real-world visual learning applications and correcting all label mistakes can be prohibitively costly. Training neural network classifiers on such noisy datasets may lead to significant performance degeneration. Active label correction (ALC) attempts to minimize the re-labeling costs by identifying examples for which providing correct labels will yield maximal performance improvements. Existing ALC approaches typically select the examples that the classifier is least confident about (e.g. with the largest entropies). However, such confidence estimates can be unreliable as the classifier itself is initially trained on noisy data. Also, naïvely selecting a batch of low confidence examples can result in redundant labeling of spatially adjacent examples. We present a new ALC algorithm that addresses these challenges. Our algorithm robustly estimates label confidence values by regulating the contributions of individual examples in the parameter update of the network. Further, our algorithm avoids redundant labeling by promoting diversity in batch selection through propagating the confidence of each newly labeled example to the entire dataset. Experiments involving four benchmark datasets and two types of label noise demonstrate that our algorithm offers a significant improvement in re-labeling efficiency over state-of-the-art ALC approaches.
A Phenomenological Approach to Film Sound and Film at the Basis of Film-Making

This chapter elaborates on the presented phenomenological approach to film sound and film at the basis of film-making. This chapter closes with case studies that offer insights into the way a phenomenological approach to sound and listening in film can be implemented in the course of creating a film and in collaboration with sound designers and composers. Phenomenology is an attitude rather than a prescribed workflow to follow. Always starting from experience, a phenomenological approach is very useful in the education of film-makers.
Semantic Image Quality Assessment Using Conventional Neural Network for E-Commerce Catalogue Management

Managing catalogues is an important challenge in the field of electronic commerce because it can significantly help visitors efficiently select the items that interest them. In retail websites, all the items included in the catalogue are displayed in a particular order and classified into different categories. This manual grouping and ordering take plenty of time. Furthermore, the evaluation of image quality plays a very important role in managing the catalogue [5]. In fact, the quality of the images sent by the supplier is not always considered as having good quality which may lead to client dissatisfaction. To deal with these issues, we propose a new approach that aims to automatically manage the image catalogue and efficiently improve the quality of displayed images. The proposed approach is based on the design of a new no-reference Semantic Image Quality Assessment method using a conventional neural network (CNNs_SIQA) that employs a deep learning technique for perceived automatically assessing image quality. Obtained results have shown the effectiveness of our proposed approach in the automatic management of catalogues and in the quality improvement of displayed images.
FASENet: A Two-Stream Fall Detection and Activity Monitoring Model Using Pose Keypoints and Squeeze-and-Excitation Networks

Numerous frameworks have already been proposed for vision-based fall detection and activity monitoring. These works have leveraged state-of-the-art algorithms such as 2D and 3D convolutional neural networks in order to analyze and process video data. However, these models are computationally expensive which prevent their use at scale for low-resource devices. Moreover, previous works in literature have not considered modelling features for simple and complex actions given a video segment. This information is crucial when trying to identify actions for a given task. Hence, this work proposes an architecture called FASENet, a 1D convolutional neural network-based two-stream fall detection and activity monitoring model using squeeze-and-excitation networks. By using pose keypoints as inputs for the model instead of raw video frames, it is able to use 1D convolutions which is computationally cheaper than using 2D or 3D convolutions, thereby making the architecture more efficient. Furthermore, FASENet primarily has two streams to process pose segments, a compact and dilated stream which aims to extract features for simple and complex actions, respectively. Furthermore, squeeze-and-excitation networks are used in between these streams to recalibrate features after their combination based on their importance. The network was evaluated in three publicly available datasets, the Adhikari Dataset, the UP-Fall Dataset, and the UR-Fall Dataset. Through the experiments, FASENet was able to outperform prior state-of-the-art work on the Adhikari Dataset for accuracy, precision, and F1. The model was shown to have the best precision on the UP-Fall and UR-Fall Datasets. Finally, it was also observed that FASENet was able to reduce false positive rates compared to a previously related study.
Content Summarization: Journey Toward Word Clouds

Generating word cloud is an illustration demonstration of the occurrence of terminology within document. It is used to imagine the frequency of words in textbook, passage, manuscript, content, scientific data, speeches, and website tags. Sources of unstructured data varies from Emails, Text files considering Word processing, spreadsheets, PDF files, Websites, Social Media, Media such as images, video, audio, Mobile data, communication including live chat, collaboration software, IM, Customer-generated content, Raw data from marketing research, Books, magazines, and newspapers, Medical records. Word cloud are recognized as content clouds describe works simply with a concept maximum number of occurrences of a word associated with different data sources visualizes superior in the word cloud. It is an assortment or group of words represented in dissimilar sizes. More important words are those which display bolder and bigger in size. It means word appear maximum number of times in input document. It generates informative words and ideal way to generate excellent data visualization aid to our brain. It is useful for business and decision-making process to summarize information. Here, we discussed various online tool used for generation of word cloud and applied online tool for generation of word cloud. For security purpose, we also used python code implementation to generate word cloud.
Aplikasi Website Pengamanan File Dokumen Menggunakan Kriptografi RSA

Data security is something that needs to be considered in maintaining the confidentiality of information, especially those that only contain information that can be known by the authorized party. There are still many cases of data leakage that occur in Indonesia, especially in documents. Documents can be secured using cryptographic techniques. One of the well-known cryptography is RSA Cryptography. The security of RSA cryptography lies in the difficulty of factoring large numbers into prime factors. The implementation of RSA cryptography will be made using the python programming language based on the website. The system created has a success rate of 100% in encrypting documents for each document, and in decrypting it has a success rate of 85% to 96%.
The Depth Buffer

How do you make sure objects get rendered in the correct order if they overlap in the viewport? What if an object is only partially obscured by another one? When attempting to render a given pixel, we need a system in place that allows us to check whether a pixel has already been drawn at the same position and determine whether it is in front of or behind the one we are currently drawing. This requires us to store additional data every time we draw a pixel. We can’t just store the color of the pixel; we also need to store the depth of the last pixel drawn to that position. Unity stores the color data inside a 2D array the size of the screen called the color buffer, and similarly, the depth information is stored in the depth buffer, which has the same dimensions as the color buffer. In this chapter, we will learn how the depth buffer works and create some shader effects that interact with it in different ways.
Efficient secure DevOps using process mining and Attack Defense Trees

In this paper, we propose a method to efficiently ensure security in the DevOps lifecycle through operations and development. To ensure sufficient security in DevOps, it is essential to perform sufficient threat analysis during development. However, threat analysis is generally a heavy task and difficult to apply to agile processes. In addition, existing technologies are not sufficient for security feedback from Dev to Ops and Ops to Dev, which are important elements of DevOps. In this paper, we propose a method for detecting attacks using anomaly detection from operation logs, and extracting vulnerabilities and candidate countermeasures using information such as CAPEC and CWE. Furthermore, we propose a method to determine the excess or deficiency of the countermeasure by comparing it with the Attack-Defense Trees created in the previous development. By applying our proposal to an actual development case, we confirm that the proposed method works effectively.
Enterprise Architecture Patterns and Principles

This chapter describes Enterprise Architecture solutions to common problems. Principles are organized by architecture domain and quality attributes addressed. Firstly, sixteen cross-domain principles are described; next, three business layer principles are introduced, followed by five information principles, four application principles, and seven technological principles. Section 4.8 describes IT patterns, including multi-layer IT architectures and IT architectures for high availability. Finally, IT integration patterns are discussed. At the end of the chapter, exercises are proposed.
Rudgea laevis

This datasheet on Rudgea laevis covers Identity.
Availability and Implementation of Mobile Learning Technologies among Ghanaian University Students: Case Study of the College of Distance Education, UCC

In recent years, mobile learning technologies have become increasingly prevalent but there are questions as to whether these technologies have been fully utilized in universities. Integrating mobile learning technologies into the teaching and learning environment has been a challenge for Ghanaian Universities. The purpose of this research is to identify the availability and implementation of mobile-learning technologies among university students and to educate institutions on how mobile technology can be helpful in the delivery of instructions. This paper further examines if mobile devices have positive impact on students’ academics by assessing wider opinions and experiences of students with the use of such devices taken into consideration anytime and anywhere learning opportunities these devices offer. It also provides some pointers to possible solutions and consider the role mobile learning technologies can play in instruction delivery based on the theory that Mobile learning is a combination of the interactions between learners, their devices and other people.
The Role of Vlog Project in Facilitating Authentic Speaking Assessment

To date, video blog (vlog) has been identified as a convenient learning tool to develop learners’ speaking skill. In language assessment, especially speaking, vlog has met the criteria for assessing learners’ speaking skill authentically since it provides real-life situations in the process of creating the video. This study aimed at investigating how vlog facilitates authentic assessment in measuring students’ speaking skill. This study is guided under a descriptive qualitative method using interview guidelines to collect the data and observation checklist in analysing the data. Two lecturers and six tertiary students from spoken English class were involved. The findings illustrate that the vlog project has acquired the criteria of authenticity of a speaking assessment as well as students’ preference in speaking assessment. The vlog project, therefore, can be advantageous media to assess students’ speaking skills authentically.
Working with Data

You can store and manipulate all different types of data: a number, a string, list, and so on. With Python, you don't need to declare all your variables before you use them.
STRUCTURAL INVESTIGATION OF THE NORTH MOUNTAIN THRUST ZONE THROUGH BEDROCK MAPPING OF THE ORKNEY SPRINGS (VA) 7.5’ QUADRANGLE

The North Mountain Thrust Zone (NMTZ) is an enigmatic structure in the central Appalachians that prior workers have interpreted as both the sole thrust of the Blue Ridge anticlinorium and as part of a duplex accommodating the change in stratigraphic level of the décollement, feeding displacement to the foreland along a basal Cambrian décollement that steps up to an Ordovician shale horizon. In this study, we present new 1:24,000 scale mapping of the Virginia portion of the Orkney Springs 7.5’ quadrangle to address the role of the North Mountain Thrust Zone (NMTZ) in transitioning from basement involved thick-skinned deformation of the Blue Ridge Province to thin-skinned deformation in the Valley and Ridge Province. Cambrian-Devonian carbonate and siliciclastic strata are folded and faulted, forming northeast trending (30°-44°) structures that record deformation from the Late Paleozoic Alleghenian Orogeny. The northeast striking NMTZ comprises several splay faults that juxtapose folded horses of the Cambrian-Ordovician passive margin carbonates above Ordovician-Devonian siliciclastic rocks of the Taconic and Acadian foreland basins in the footwall to the west. In the footwall of the NMTZ, the Ordovician-Devonian strata is folded into the overturned Supin Lick Syncline and Adams Run Anticlinorium, forming the Alleghenian floor sequence. The north central portion of the map area is characterized by north trending (~5°-10°) tight to isoclinal folding of the Silurian-Lower Devonian systems on the shared upright limb of the Supin Lick and Adams Run structures. These structures, which are not documented on existing maps of scale < 1:24,000, are subparallel to a north trending cross fault that offsets the western limb of the Supin Lick Syncline and may suggest a transpressional component to strain along the NMTZ. We use newly collected structural data and updated mapping to constrain the structure of a thrust duplex that accommodates a vertical step in the stratigraphic level of the NMTZ from the sole thrust of the Blue Ridge Province to the décollement in the Valley and Ridge Province. Our results will help to better understand the kinematic transition from thick-skinned basement involved deformation to thin-skinned foreland fold-thrust belts in collisional orogens.
Supplementary Data for Appendix 9.1

Obsidian artifact and geologic specimen description and parts per million (ppm) elemental concentrations. Online supplementary data.
Contrastive Knowledge Transfer for Deepfake Detection with Limited Data

Nowadays forensics methods have shown remarkable progress in detecting maliciously crafted fake images. However, without exception, the training process of deepfake detection models requires a large number of facial images. These models are usually unsuitable for real world applications because of their overlarge size and inferiority in speed. Thus, performing data-efficient deepfake detection is of great importance. In this paper, we propose a contrastive distillation method that maximizes the lower bound of mutual information between the teacher and the student to further improve student’s accuracy in a data-limited setting. We observe that models performing deepfake detection, different from other image classification tasks, have shown high robustness when there is a drop in data amount. The proposed knowledge transfer approach is of superior performance compared with vanilla few samples training baseline and other SOTA knowledge transfer methods. We believe we are the first to perform few-sample knowledge distillation on deepfake detection.
IMMFV2: Improved Modified Fermat Factorization Algorithm

Network security has intense significance and worth in the protection of the system from unauthorized access or threats. Denial of service, illicit access, executing commands illegally, confidentiality violation and breaching, data diddling and destruction of data are the main threats which a network may have to cope up with. Numerous techniques and algorithms are extensively implemented and manipulated in order to enhance the security and safety of networks and to make it unbreakable and inaccessible to unauthorized and unofficial access and attacks. Cryptography has its own importance in network security. RSA is the public key cryptosystem. The RSA algorithm is most extensively used in network security. In this paper there is comparison and analysis of RSA factoring Algorithms with our proposed technique. We will analyze and evaluate the performance of different Fermat Factoring Algorithms which are Modified Fermat Factorization and Modified Fermat Factorization V2 and its comparison with our proposed Improved modified Fermat Factorization V2. The calculations have been given in detail and these calculations have been calculated and evaluated in net beans software. And the comparison will be demonstrated in experimental results. The comparison after calculation will show that IMMFV2 is better in implementation as compared to MFF and MFFV2.
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation

Abstract This paper presents a deep neural network based approach for interactive digital transformation and simulation of n-bar planar linkages consisting of both revolute and prismatic joints from hand-drawn sketches. Instead of taking a pure computer vision approach, we combine the output of a convolutional deep neural network with the topological knowledge of linkage mechanisms to create a framework for recognition of hand-drawn sketches. To accomplish this, we first synthetically generate a dataset of images of linkage mechanism sketches similar to hand-drawn ones and then fine-tune a state of the art deep neural network capable of detecting discrete objects. While the network had previously been exposed to only a general class of images of every-day objects, it was for the first time trained with a set of building blocks of linkage mechanisms, viz. joints and links. Thereafter, we present a novel algorithm, which performs topological analysis on the set of detected objects to create a kinematic model of the sketched mechanisms. The results show that this algorithm performs well on hand-drawn sketches and could help with conversions of such sketches to their digital representation for effective communication, analysis, cataloging, and classification.
An Improved Dissimilarity based Approach to Semantic Similarity Calculation

With the trend that the computational process of semantic similarity more and more mimics the human thought process, it becomes very important to consider the difference between semantics. In this paper, we review commonly used semantic similarity models, discuss the limitations of our previous models, and improve the information content (IC) computation model based on wordnet ontology. In turn, different strategy is employed. The results show that the model proposed in this paper has high computational accuracy.
A Comprehensive Survey of Web and Mobile Apps for Fishermen

Mobile and web apps are playing an essential role in people’s daily life. ICT enhances internet connectivity to anyone and anything. In this case, the importance of mobile apps is critical in extreme environmental conditions such as ocean scenarios. Several mobile phone applications have been launched to encourage and assist fishermen through knowledge distribution, facilitating communication, monitoring geolocation, and assisting in boat-to-shore activities. However, these apps need internet connectivity to provide services. They are limited to working only on the shore using cellular connectivity. In this context, OceanNet, a marine communication infrastructure that our research center has developed and deployed in 2016. OceanNet provides affordable internet access up to 60 km from the shore. We have interviewed around hundred fishermen in the neighborhood to understand their requirements. We have also identified a few mobile and web applications developed for the fisherman. In this paper, we analyze and summarize the features provided by these web and mobile applications. We also discuss the requirements of the fisherman when they are sailing. In the future, we plan to develop suitable applications to address the gaps identified in this study.