Reviewerly/exHarmony huggingface dataset:
Solution Validation and Customer Needs Understanding in the Early Phases of Product Platform Development; a Case Study in Digital Manufacturing Machines

Abstract We have observed in our company that the high‐level specification of new products is difficult. The company works with legacy product platforms. Even with extensive knowledge about legacy products and applications, it can be difficult for engineers to understand if their proposed solutions for new products or features that will satisfy customers. The goal of this study was to investigate why the company is having problems with specifying new products that eventually will constitute a new product platform. We show that relying on proxies and agile development is not sufficient to close the validation loop, how validation of solution concepts fails due to insufficient specifications of needs and that engineers must rely on indirect customer feedback, which they often receive late in the development process. We trace the problem to a suboptimal definition of customer segments, causing a lack of validated needs and leading to challenges in calculating business cases
Efficiency Improvement in Reactive Absorption of Nitrogen Oxides

This study is focused on reactive absorption of nitrogen oxides created during calcium nitrate fertilizer production. A functional mathematical process model was set up to verify the process by experimental data and to devise modifications of a real plant with this technology. Suggestions for nitrogen oxide removal efficiency improvement, including changes in operational parameters and plant layout were simulated in Aspen Plus, to achieve compliance with the European legislative emission limit of 300 ppm (mg/m3) of NOx in the off-gas released to the atmosphere. Neither of the suggested process modifications, be it an increase in column packing specific area, increase in packing height, liquid recycles cooling to ambient temperature or make-up absorbent flow rate increase led to sufficient NOx concentration decrease. Addition of a H2O2 solution into the second column was proposed to scavenge the remaining NO as it contributes mostly to the overall NOx emissions. Simulation results indicate this as a viable option to both meet the legislative criteria and to avoid liquid streams' contamination so they can be reprocessed instead of being dealt with as waste.
A Computational Approach for Secure Cloud Computing Environments

The recent emergence of cloud computing has drastically altered everyone's perception of infrastructure architectures, software delivery, and development models. Projecting as an evolutionary step, cloud computing encompasses elements from grid computing, utility computing, and autonomic computing into an innovative deployment architecture. Cloud computing systems offer a lot of advantages like pay per use and rapid service provisioned on demand, while it suffers for some concerns specially security. In fact, a number of unchartered risks and challenges have been introduced from this new environment. This chapter explores the security issues in cloud computing systems and shows how to solve these problems using a quantitative security risk assessment model.
Copy Move Image Forgery Detection with Exact Match Block Based Technique

Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16. It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.
Select the Best Translation from Different Systems Without Reference

In recent years, neural machine translation (NMT) has made great progress. Different models, such as neural networks using recurrence, convolution and self-attention, have been proposed and various online translation systems can be available. It becomes a big challenge on how to choose the best translation among different systems. In this paper, we attempt to tackle this task and it can be intuitively considered as the Quality Estimation (QE) problem that requires enough human-annotated data in which each translation hypothesis is scored by human. However, we do not have rich data with high-quality human annotations in practice. To solve this problem, we resort to bilingual training data and propose a new method of mixed MT metrics to automatically score the translation hypotheses from different systems with their references so as to construct the pseudo human-annotated data. Based on the pseudo training data, we further design a novel QE model based on Multi-BERT and Bi-RNN with a joint-encoding strategy. Extensive experiments demonstrate that our proposed method can achieve promising results for the task to select the best translation from various systems.
Point-Cloud Rendering for Video See-Through Augmented Reality

In the context of the video see-through augmented reality, the real-world scene captured by the camera should be rendered suitably for stereoscopic display. This paper proposes a real-time rendering method for stereoscopic images using a commodity RGBD camera. It casts a ray toward each pixel and the 3D points close to the ray are interpolated. This method successfully fills the holes in the stereoscopic images.
INTERPRETATION OF THEATRICAL THEME IN GRADUATION WORKS OF ILYA GLAZUNOV ACADEMY STUDENTS

The article for the first time examines the interpretation of the theatrical theme in the graduation works of the Faculty of Painting of Ilya Glazunov Russian Academy of Painting, Sculpture and Architecture within the chronological framework of the end of the XX - the beginning of the XXI century. The pictures are analysed from the perspective of realistic school taking into account the author’s individual style. Justifying multi-component integrity of authors’ methodologies, imaginative and meaningful decisions, the paper concludes that synthesized nature of artistic language is an important characteristic of modern creativity within the realistic trend.
Multiple Embedding Process for Increasing the Capacity of the Embedded Secret Message

Recently, almost all places around the world are connected by the internet. As the main requirement, the network must be safe for transmitting data. Therefore, the need for network security is now quite urgent in the field of digital communication. Protecting the secret message between the sender and the receiver during data transmission is a necessity. One of the popular network security mechanisms is called steganography. It secures the data by hiding the secret message in a cover image so that those who are not entitled to access the data will not realize that there is a secret message hidden in that image. A better steganographic method to use has a better effect on the secret message being hidden. The first aspect that can be used to determine the quality of the steganographic method is the degree of the similarity between the image files that have been embedded a secret messages (stego image) and the image files that have not been embedded secret messages (cover image). The second aspect is the payload capacity, the number of secret messages that can be embedded into the cover image. In this research, a new method of steganography is developed whose aim is to increase the payload capacity. The final result of the experiment shows that the new method can increase it. Moreover, this performance only has a slight effect on the decrease in the quality of the stego image.
Motivationally Targeted View for Architectural Modeling of Software Intensive Systems

This paper focuses on explicit considering of motives and goals at the stage of design thinking when designers try to understand the system to be designed as a wholeness. For this, it is advisable to apply the architecture approach, developing an initial complex of the essential views of the corresponding architectural description. The complex must not only provide an initial understanding of the future system but also demonstrates the conditions under which the intended goals will be attained. We propose a version of creating the complex by solving the systems of conceptual equations, any of which corresponds to the cause-and-effect discourse of an abductive kind. The version is realized in the instrumental environment that supports automated design thinking.
Keyword-Based Indonesian Text Summarization

Automatic summarization is a hot research topic in the field of natural language processing. Now the automatic summarization technology is mostly for majority languages such as English and Chinese, while less for the rare languages such as Indonesian. We aim to analyze the development trends of summarization and explore the processing methods for Indonesian summarization. This paper introduces the implementation process and experimental results of Indonesian text summarization based on the keyword frequency extraction method. The experimentation compares the RUGOE-2 result of our keyword-based system with that of the PSKSUMSUM one in Indonesian text summarization. The experimental results show that the keyword-based Indonesian text summarization is more effective.
Rates of contraction with respect to $L_{2}$-distance for Bayesian nonparametric regression

We systematically study the rates of contraction with respect to the integrated $L_{2}$-distance for Bayesian nonparametric regression in a generic framework, and, notably, without assuming the regression function space to be uniformly bounded. The generic framework is very flexible and can be applied to a wide class of nonparametric prior models. Three non-trivial applications of the framework are provided: The finite random series regression of an $\alpha$-Holder function, with adaptive rates of contraction up to a logarithmic factor; The un-modified block prior regression of an $\alpha$-Sobolev function, with adaptive-and-exact rates of contraction; The Gaussian spline regression of an $\alpha$-Holder function, with near optimal rates of contraction. These applications serve as generalization or complement of their respective results in the literature. Extensions to the fixed-design regression problem and sparse additive models in high dimensions are discussed as well.
46.5: Computational Correction Method for Optical Distortion in Eyewear Displays

In this paper, we give a brief introduction of eyewear displays for virtual reality (VR) and augmented reality (AR), and present two computational methods that not only can be applied to traditional eyewear displays but systems with asymmetrical and nonlinear distortion as well. The methods combine image segmentation with linear approximation to correct the optical distortion. Through these approaches, pixel‐mapping table for generating pre‐distorted image can be established simply and quickly. The feasibility has been verified by experiments. These distortion correction methods for eyewear displays can achieve a sufficient accuracy, avoid a great deal of numerical calculations and enable real‐time correction.
Promoting Work‐Life Balance as Chair

The Department ChairVolume 30, Issue 2 p. 15-16 Articles Promoting Work-Life Balance as Chair Michael LaGier, Michael LaGierSearch for more papers by this author Michael LaGier, Michael LaGierSearch for more papers by this author First published: 01 October 2019 https://doi.org/10.1002/dch.30284Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume30, Issue2Fall 2019Pages 15-16 RelatedInformation
Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN)

Lampung has the only breeding of in situ butterflies engineered in Indonesia namely Gita Persada Butterfly Park, which has approximately 211 butterfly species. Butterflies can be classified according to patterns found on the wings of a butterfly. The weakness of the human eye in distinguishing patterns on butterflies is a foundation in building butterfly identification based on pattern recognition. This study uses 6 species of butterflies: Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, and Pachliopta aristolochiae. The butterfly dataset used is 600 images. The butterfly image used is in the form of the upper wing side. The pre-processing stage uses the method of scaling, segmentation, and grayscale. The feature extraction stage uses the canny edge detection method by applying smoothing, edge strength, edge direction, non maximum suppression, and hyterisis thresholding. The classification phase uses the K-Nearest Neighbor (KNN) method with values k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 obtained under the Rule of Thumb. The identification of butterfly require a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%.
What Nerve Complexes Tell Us About Image Shapes

This chapter explores what nerve structures in cell complexes tell us in approximating shapes revealed by light reflected from curved surfaces. Recall that a nerve, in its simplest form, is a collection of nonempty sets that overlap. That is, the parts of a nerve have nonempty intersection. This simplest form of a nerve was introduced by H. Edelsbrunner and J. L. Harer in their monograph on Computational Topology [1, Sect. 3.2, p. 59]. Of great interest here are the two forms of nerve complexes introduced by Alexandroff [2], namely.
Kent Beck or Pablo Picasso? Speculations of the Relationships Between Artists in Software and Painting

The way software is created is somehow similar to the process of creating pieces of artwork. To consider this issue further we have considered the similarities between the software development process and painting in the quest for artistic practices that are transferable to software.
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding

Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning.Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point.During this process, the adversarial example can be further optimized, even when it has already been wrongly classified with 100% confidence, thus making the adversarial example even more difficult to detect.For this kind of adversarial examples, which we refer to as over-optimized adversarial examples, we discovered that the logits of the model provide solid clues on whether the data point at hand is adversarial or genuine.In this context, we first discuss the masking effect of the softmax function for the prediction made and explain why the logits of the model are more useful in detecting over-optimized adversarial examples.To identify this type of adversarial examples in practice, we propose a non-parametric and computationally efficient method which relies on interquartile range, with this method becoming more effective as the image resolution increases.We support our observations throughout the paper with detailed experiments for different datasets (MNIST, CIFAR-10, and ImageNet) and several architectures.
Biopsy Robot Design for Breast Cancer Identification

Nowadays breast cancer is one of the main problems that affect women worldwide. In Mexico, this disease is considered the second cause of death of productive age women, and the first cause of death worldwide. This death rate, as well as the incidence, have been ascending in the last years; however, the correct interpretation of test analysis could play a key part in the process. Biopsy procedures are successful only for 78% of cases, which leaves 22% of patients in danger of receiving wrong diagnoses which include false negatives, leading towards late treatment and therefore fewer survival possibilities. This project aims to increase the successful biopsy results by implementing the usage of biomedical robotics. The development consists of a Delta type robot, equipped with infrared sensors for cancerous breast pathology localization, trucut type biopsy needle, thermal image projection, artificial intelligence for optimum trajectory and positioning over the patient. This equipment offers a live detection with infrared technology and a precise incision controlled by the health professional. Therefore, this system offers a more accurate precision reaching the tumour, achieving a better extirpation of suspicious tissue for histopathological analysis, reducing the number of false negatives and, therefore, acting directly over the death rate.
Human Factors in Automated Analysis for Enterprise Models

Enterprise models are created for communicating and documenting the current state of the enterprise. However, these models can also be used for supporting analysis processes and are fundamental assets in project management. But, analysis is a process made by humans, and due to enterprise models that are complex and have a large amount of elements, analysis is usually a tough process. Then modeling tools might provide support for analysis. It is possible to offer this support through the use of automated analysis methods, which are algorithms for providing specific calculations based on the elements included in the model. The results of said automated analysis methods support decision-making processes. It is also possible to execute a sequence of analysis methods by the configuration of analysis chains. This chapter presents a proposal and strategy for analyzing enterprise models by the execution of automated analysis methods and automated analysis chains. This strategy is presented using enterprise models that conform to ArchiMate as modeling language.
An Efficient Vision-Based Group Detection Framework in Crowded Scene

Visual surveillance systems are now widely used for monitoring the events. The challenging task in these events is effectively analyzing the crowd and its behavior. For better understanding the behavior of crowd or analyzing it, the group is considered as the basic element. The exigent task in a crowded scene is to distinguish between the groups and individuals. In this paper, we have proposed a video-based framework that efficiently identifies the group of people from the crowd. The framework is composed on boundary extraction of a group called contours in the literature. The proposed approach makes use of background subtraction algorithm called ViBe, to extract the relevant features and incur contours in the video frames. Further we detect the group in a crowd on the basis of threshold frames obtained by calculating the area and distance between them. Analysis has been carried out on a self-gathered dataset from the university campus. The proposed framework is able to distinguish the group and no group with an average accuracy of 86.06%.
Identification of Age and Gender in Pinterest by Combining Textual and Deep Visual Features

In social media users share a lot of content, such as comments, news, photos, videos, etc. This information can be used by automated systems to segment the users to provide them with specific recommendations or focused content. One of the most popular way to segment the users is by age and gender. Nevertheless, such demographic variables are frequently hidden, and thus becomes useful to indirectly infer them. Commonly, these variables are learned using the text comments the users publish, analyzing the style of writing or frequency of words. In this paper, we present a study of several machine learning models that employ user generated images and text trying to exploit both types of information to infer the age and gender for Pinterest users. We experiment with the models using a dataset composed of 548,761 pins, posted by 264 users. Each pin is a combination of an image and a short comment. We transformed the images to a deep visual representation using the pretrained convolutional neural network ResNet-50, and transformed the comments using the tf-idf method. We compare the models among them and between the types of information using different performance metrics. Our experiments show interesting results and the viability of employing the user generated image and text content to characterize users.
Offline Cursive Handwritten Word Using Hidden Markov Model Technique

Hidden Markov Model (HMM) based offline cursive manually written word segmentation technique is proposed in this strategy. In this paper, we are utilizing a classification technique to perceive the written by hand word which is SVM. Dataset collection comprises handwritten words which are in the cursive configuration images are taken as input and these pictures comprise of noise and these noises are expelled by preprocessing strategy. The preprocessing technique incorporates word picture acquisition which is an RGB image; for additional steps, the RGB image is changed over to gray image. Later, thresholding is applied to the gray image. Thinning and skeletonization is connected to the thresholded image. At that point, noise is expelled from the manually written word image and a preprocessed binary matrix appears as a matrix. Over-segmented words are partitioned by potentially segmented column (PSC) and the HMM technique. At last, the character is perceived by utilizing SVM Method.
Numerical simulation of indigenous wheelchair for ease of mobility

One of the most common problems hospital staffs are now facing is shifting patients between bed and wheelchair. This situation occurs often during pre- or post-operation. Thus, considering this problem we propose the design of a structure which functions as wheelchair, bed, and stretcher. This will help a lot of patients during an emergency by decreasing their discomfort and saves some time. This will also help fractured patients avoid unnecessary movements. The recent studies in this area have seen some development but have been unsuccessful in reaching a feasible solution. This design attempts to achieve convenient functioning whilst quick motion. The unique feature that varies this from other wheelchair is that it has a mechanical control for the transition which can be controlled by the patients themselves without any help. It also allows the patient to increase the height of the bed and allow easy access for doctors and increase their comfort. For this elevation, a simple screw jack mechanism is used. The backrest and leg rest inclination mechanisms include radial springs and cam locking mechanism respectively. Hence the structure becomes self-sufficient and simple to handle. The final outcome of this proposal would be achieving low-cost commercial wheelchair which can be converted into a bed and a stretcher. Technological developments find its way into the field of Medicine supporting to solve its problems. Thus, our wheelchair would be one among the supports helping who are in need.