Reviewerly/exHarmony huggingface dataset:
Domain Knowledge: Predicting the Kind of Content Hosted by a Domain

In a broad set of domains, from protecting people from harmful content to segmenting online customers, we need to know the kind of content hosted by a web domain. But there are nearly 2 billion unique hostnames today and curated domain label lists carry at best a few million domains. We bridge the gap by exploiting labeled data from multiple large, curated lists—Shallalist, PhishTank, Malware Domains, and Squidguard—to build models that predict the kind of content hosted by a domain using the sequence of characters in the domain name. Given identifying domains that carry harmful material or adult content is particularly important, we primarily focus on those categories. The models do very well at predicting domains that host pornographic content, with f1-scores of about .9 or higher. We are less successful at predicting domains that carry harmful content with f1-scores of two of our best models around .8. To illustrate the utility of our models, we use them to answer two questions: 1. Do poor people, racial or ethnic minorities, and the less well-educated visit malware sites more often than their respective complementary groups, and 2. Does the consumption of pornography vary by age and education?
Teaching Particle-Wave Duality with Double-Slit Single-Photon Interference in Dutch Secondary Schools

A lesson series on quantum physics was taught to grade 12 students in 5 different secondary schools. Among others, the series contained a special demonstration experiment for particle-wave duality (double-slit single-photon interference) which was supplemented by the PhET applet quantum wave interference. Preconceptions on particles and waves were assessed in a pretest. Interviews were conducted with 24 students in 4 schools after the demonstration. A posttest was administered to 112 students in 3 schools. Some lessons were observed by an outside observer and/or videotaped, followed by interviews. Results indicate that learning objectives of quantum physics are well within reach of the 12th grade students. This shows especially in student discussions of the PhET applet. However, understanding of wave-particle duality is hindered by insufficient understanding of the prerequisite wave concept that waves are spread out in space. Most students associated a wave with an oscillation and somehow forgot about the spatial aspects. As a consequence, the “strangeness” of wave-particle duality was underappreciated. Also, the link with the de Broglie wavelength discussed early in the lesson series was not made. As in the 5th school quantum physics started several weeks later, we were able to try an improved lesson design for the demonstration successfully. Interviews are very helpful in catching misinterpretations of demonstrations and generating suggestions for improvement. Students do appreciate the confusing wonders of quantum physics but in the post-interviews seem unaware of the many quantum-based devices around them and the many everyday phenomena (like color) which need quantum physics for explanation. Perhaps as teachers and curriculum developers, we are too eager to overemphasize the exotic and strange aspects of quantum theory and underemphasize utility in everyday applications.
Proposed Model of Academic Reading Material Recommendation System

Cold-start problem and cold-item are something that will happen when an early developed online library of educational institution library tries to recommend scientific articles to users. The reading materials do not even have reviews and/or ratings from previous users, no users have expressed preferences yet, also solely rely on keywords in search engines. The fact that there are abundant holdings in the library, it needs to effectively maintain users' interests to borrow and download academic reading material in accordance with users' interest from holdings in the library repository. This study seeks to provide novelty by finding another way to utilize dataset with only using abstract and title variables as an input parallelly that can provide effective results as a recommendation system. It proposes a word embedding model to be used as topic modeling for the content-based recommendation system to overcome the problems, wherein the attributes are minimum (such as title, author, and abstract) and user data are not available.
Application of Digital Image Segmentation of Plantation Fruit Classification in Samarinda Agricultural Polytechnic

Applications of Digital Image Segmentation of Plantation Fruit Classification in Samarinda State Agricultural Polytechnic Based on Form The development of computer technology at this time has brought significant progress in various aspects of human life. Such development is supported by the availability of increasingly high hardware and software, one of the technologies experiencing rapid development is image processing. Image processing is a system where the process is carried out by entering an image and the result is also an image. Currently the use of digital images is widely used in various fields one of which is in the plantation sector. Therefore, the purpose of this study is to create a digital image segmentation application for the classification of plantation fruit based on shape. The method used for image segmentation is the Thresholding method, while the image classification uses the Artificial Neural Network (ANN) method. The accuracy generated by the system both in the training process and testing shows that the method used can classify fruit images well
Domain Adaptation via Image Style Transfer

While recent growth in modern machine learning techniques has led to remarkable strides in computer vision applications, one of the most significant challenges facing learning-based vision systems is the scarcity of large, high-fidelity datasets required for training large-scale models. This has necessitated the creation of transfer learning and domain adaptation as a highly-active area of research, wherein the objective is to adapt a model trained on one set of data from a specific domain to perform well on previously-unseen data from a different domain. In this chapter, we use monocular depth estimation as a means of demonstrating a new perspective on domain adaptation. Most monocular depth estimation approaches either rely on large quantities of ground truth depth data, which is extremely expensive and difficult to obtain, or alternatively predict disparity as an intermediary step using a secondary supervisory signal leading to blurring and other artefacts. Training a depth estimation model using pixel-perfect synthetic depth images can resolve most of these issues but introduces the problem of domain shift from synthetic to real-world data. Here, we take advantage of recent advances in image style transfer and its connection with domain adaptation to predict depth from a single colour image based on training over a large corpus of synthetic data obtained from a virtual environment. Experimental results point to the impressive capabilities of style transfer used as a means of adapting the model to unseen data from a different domain.
Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Depression is a widespread and serious phenomenon in public health in all societies.In Saudi society, depression is one of the diseases that the community is may refuse to disclose it.There are no studies have analyzed this disease within the Saudi community.The main research objective is to discover the depression level of Saudi People's.In addition to analyzing the age group and the most gender type affected by the depression in this society.The data collected from social media achieved indirectly without any communication with patients as a sample from this society people.It analyzed using Machine Learning algorithms that give accurate results for this disease.Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five classes and the most affected age group in depression was in the age group from 20-26 years.The results show that young Saudi women are more likely to be depressed.The obtained results are very important to the medical field.Researchers and people working in this field can get benefits out of this research.Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.
Patch Relational Covariance Distance Similarity Approach for Image Ranking in Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) is an information retrieval framework for retrieving similar images based on objects in the images. Machine learning based CBIR consists of object detection, the majority of which rely on Convolutional Neural Network (CNN) as object detector, and image similarity ranking. However, object detection with CNN requires expensive retraining when new set of the images is added to the database, while current ranking techniques focus on visual characteristics without considering object's spatial information.
An Empirical Study on the Satisfaction of New Form Textbook Based on ACSI

New form textbook, also known as online and offline integrated textbook, has been vigorously promoted in B-learning model these years, as the basic basis of teaching activities, what factors will affect students' satisfaction towards it, how to optimize and improve the influencing factors are all worthy of attention. Therefore, this study constructed a new form textbook satisfaction model from the perspective of students by referring to the core concept of ACSI. The results showed that the perceived quality of platform, resource, knowledge form and discussion area were positively correlated with satisfaction, perceived value and loyalty (P < 0.05), the standardized path coefficients of perceived quality and perceived value to satisfaction were 0.219, 0.597 respectively, and the standardized path coefficient of satisfaction to loyalty was 0.884, which was statistically significant and the model fitted well. According to the fitting results of the model, the optimization suggestions for the four influencing factors were put forward, which can provide reference for the evaluation and optimization of new form teaching textbook.
Counting Livestock with Image Segmentation Neural Network

Livestock farming industries, as well as almost any industry, want more and more data about the operation of their business and activities in order to make the right decisions. However, especially when considering very large animal farms, the precise and up-to-date information about the position and numbers of the animals is rather difficult to obtain. In this contribution, a novel engineering approach to livestock positioning and counting, based on image processing, is proposed. The approach is composed of two parts. Namely, a fully convolutional neural network for input image transformation, and a locator for animal positioning. The transformation process is designed in order to transform the original RGB image into a gray-scale image, where animal positions are highlighted as gradient circles. The locator then detects the positions of the circles in order to provide the positions of animals. The presented approach provides a precision rate of 0.9842 and a recall rate of 0.9911 with the testing set, which is, in combination with a rather suitable computational complexity, a good premise for the future implementation under real conditions.
Photorealistic Image Synthesis Using SPADE Algorithm

Synthesizing high-resolution photorealistic images is playing a vital role in construction of user control on semantic image information in visual processing applications. This is a major challenge in deep learning to address both semantic control and style in synthesizing images. The existing deep learning models will take input as semantic layout and processed through a number of convolution, normalization, and nonlinear layers. In this process, the semantic information of a given image is lost due to multilayer process. This paper explains an alternative approach to address the said issue with SPADE algorithm. Training and testing the model was performed with various sizes of data from COCO dataset.
Gaussian Distribution Operator Based Spider Monkey Optimization Algorithm to Improve Global Search Ability

Spider monkey optimization algorithm is a nature-inspired meta-heuristic fission–fusion system based algorithm used to obtain the optimum solution of complex optimization functions. The main concept behind this algorithm is an interesting foraging behavior of spider monkeys, they are Fission–Fusion Social Structure (FFSS) based animals. In order to improve the global search performance and to overcome the deficiency of impulsive convergence of SMO, a new variant of this algorithm is presented and named as Gaussian Spider Monkey Optimization (GSMO) algorithm. In the proposed algorithm an additional distribution parameter is introduced in the fundamental search equation of SMO algorithm in the given search region. This operator can be obtained from regular Gaussian distribution. In end, the presented algorithm is tested on several standard optimization problems and also compared with Original SMO and concluded that the GSMO can outperform the SMO algorithm.
Validation of a diffeomorphic registration algorithm using true deformation computed from thin plate spline interpolation

Despite the inter and intraobserver variabilities, manual contours are commonly used as surrogates for ground truth in the validation process for nonrigid medical image registration. In contrast, this study proposes the use of thin plate spline interpolation to create a true deformation field. A diffeomorphic registration method was compared to the true deformation field along with three other algorithms and was evaluated on simulated cardiac motion deformation over 10 subjects from the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Two sequential registration approaches were undertaken: with respect to the first frame, and with respect to the previous frame. The Dice score was calculated between the simulated and warped contours for the two approaches: diffeomorphic registration method =0.991 and 0.997, RealTITracker (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> method) = 0.971 and 0.977, RealTITracker (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> method) = 0.975 and 0.978, and Elastix = 0.976 and 0.994. The results demonstrate the robust performance of the diffeomorphic registration method.Clinical relevance This establishes a validation of a registration method that can be used for segmentation of chambers of the heart.
Bootstrap technique for image detection

Through more suitability of digital imaging and computer graphics, it suits simpler to convert images content than before without any visually touches for catching these processing issues. Several fake images are created whose content is altered. The research has been reinforced by an application within MATLAB environment of a programmed searching about similar images of the saved image. The research has also been reinforced by a number of forms, pictures, and schemes that clarify the content of the research. The focus of the research lies on two important criteria depending on the content including histogram and statistical criteria of the image for every color. The steps for retrieving process has been clarified starting from statistically analyzing the image and conforming it to the image formed in the database to arrange the images according to their similarity with the target one.
Multi-objective Optimization Design in Construction Period Considering the Influence of Marine Climate

Zhang, X., 2020. Multi-objective optimization design in construction period considering the influence of marine climate. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 116-119. Coconut Creek (Florida), ISSN 0749-0208.The traditional construction schedule network plan only optimizes a certain index of construction period, resources and expenses independently, and does not comprehensively consider the joint optimization among the indexes. The network plan is often inconsistent due to the difference of selected indexes, which makes the construction unit at a loss. During the construction period of the construction project, the final construction method is mainly to control the cost of the construction project by scientific methods, to ensure the overall construction quality of the project and to improve the efficiency of the use of funds. When the construction project is not timed, the factor of sea level rise should be taken into account in calculating and designing the high water level. As the sea level rises in the depths of the ocean, the evaporation of sea water accelerates and the atmospheric temperature correspondingly increases. This paper studies the multi-objective optimization design of construction period under the influence of marine climate, which is of great significance to the construction period control of subsequent projects in order to continuously improve the quality of construction period management of enterprises and to study the construction period management methods.
Writing It Up Right: Which Document Automation Tool Is Best for Me?

Document automation is one of the few legal tech applications whose meaning is easily understood even by the most old-fashioned lawyers: The automated production of documents is not only considerably faster but often also of higher quality than manual drafting based on the documents “from last time” as a template. It can also become the basis for self-service structures with the lines of business (for an in-house legal department) or clients (for law firms). Lawyers that can answer requests from their internal or external clients, e.g. for a standard NDA, simply by referring to their document generator in the inter-/intranet also appreciate the fact that, after the wording and inner logic of a document has been programmed into a ‘smart template’, the document needed by the end user can be created without further action on the part of the human lawyer. It is therefore not surprising that many law-firms and legal departments are considering to introduce document automation into their organization—or have already done so in the past and consider upgrading to a more powerful automation tool. It is tempting to see the introduction of document automation as an IT project, i.e. as the selection and implementation of the right software. This is only logical since introducing document automation into an organization indeed typically requires the selection and implementation of a new piece of software. But: If you don’t know exactly what you want to do with document automation and who should in what way use the software in your company, you will only stumble into the right tool by chance—if at all. And more importantly: Even with a great piece of software in place, it can still happen that the end users and other stakeholders ignore the new tool because they don’t feel that it is worth the effort of changing their style of working. It is therefore important to take a clear look at the objectives to be achieved and the underlying target operating model. The desired outcome could be as diverse as mass contract repapering, template standardization through automation, qualitative control of legal issues outside the legal department or simply to enable the business. Anyone who does not view the introduction of document automation in a larger context and fails to include the analysis and optimization of the associated work processes will be working towards a “crappy digital process”, a term made famous by the former CEO of Telefonica Thorsten Dirks. And finally, while the legal tech enthusiasts in your company may be longing for the changes in their daily work that document automation brings about, other stakeholders in the company will be critical of them (and may even be right, especially if you haven’t done your job well). Whoever wants to inspire more than just the early adopters in the company should think about communication and change management ahead of time. This article provides suggestions on all the above points and summarizes them in a manageable checklist.
Hybrid Forecasting Scheme for Enhance Prediction Accuracy of Spambase Dataset

Due to elementary requisite and amplified figure of unwanted spam in network has gain high attention of investigators. A worthy amount of investigators have shown several causes of spam in network. Therefore to overwhelm the issues of handy prediction schemes of spam, a dissimilar and efficient ensemble approach has been proposed in this study that utilized the functionalities of multilayer perceptron (MLP), a form of neural network in the company of Bayesian theorem. To examine and reveal the competence of the proposed approach, it has coded in the most popular and employed open-source programming language JAVA. For signifying the effectiveness of the developed model, four different sets of data for spambase have opted from open-source database library of UCI. The weighty outcomes of the proposed system have proven its efficiency over the separate version of prediction method as well as current innovative accessible techniques.
Online Handwritten Signature Verification and Recognition Based on Dual-Tree Complex Wavelet Packet Transform.

With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication.In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT).In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov- Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset.Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects.