Reviewerly/exHarmony huggingface dataset:
Exploring the Design Space of Aesthetics with the Repertory Grid Technique

By optimizing aesthetics, graph diagrams can be generated that are easier to read and understand. However, the challenge lies in identifying suitable aesthetics. We present a novel approach based on repertory grids to explore the design space of aesthetics systematically. We applied our approach with three independent groups of participants to systematically identify graph aesthetics. In all three cases, we were able to reproduce the aesthetics with positively evaluated influence on readability without any prior knowledge. We also applied our approach to two- and three-dimensional domain-specific software visualizations to demonstrate its versatility. In this case, we were also able to acquire several aesthetics that are relevant for perceiving the visualization.
RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.
Gated Texture CNN for Efficient and Configurable Image Denoising

Convolutional neural network (CNN)-based image denoising methods typically estimate the noise component contained in a noisy input image and restore a clean image by subtracting the estimated noise from the input. However, previous denoising methods tend to remove high-frequency information (e.g., textures) from the input. It caused by intermediate feature maps of CNN contains texture information. A straightforward approach to this problem is stacking numerous layers, which leads to a high computational cost. To achieve high performance and computational efficiency, we propose a gated texture CNN (GTCNN), which is designed to carefully exclude the texture information from each intermediate feature map of the CNN by incorporating gating mechanisms. Our GTCNN achieves state-of-the-art performance with 4.8 times fewer parameters than previous state-of-the-art methods. Furthermore, the GTCNN allows us to interactively control the texture strength in the output image without any additional modules, training, or computational costs.
Text Feature Extraction and Clustering Analysis of Events Caused by the Cockpit Crew

Applying data mining technology into civil aviation events analysis can help to obtain the core contents quickly. Based on events caused by the crew members, the term frequency-inverse document frequency (TF-IDF) algorithm was used to vectorize the text features and give weights. The K-means algorithm was used to cluster analysis on the vectorized data, and the results obtained by the multidimensional scaling algorithm were visually displayed. Then, statistical analysis of the stages and causes of the events was carried out based on the cluster results. Finally, “mistakes of crew members” was taken as an example to do analysis. The results show that emphases should be paid on the three types of crew caused events, which are “approach suspension/go around”, “incomplete predetermined configuration” and “overspeed.” Furthermore, it proves that text feature extraction and cluster analysis can be applied to do analysis of human-caused events and help to conveniently and quickly clarify the direction of safety risk management.
Face Detection based approach to combat with COVID-19

Abstract The global problem at the moment is COVID-19 caused by corona-virus which led to the worldwide lockdown. However there are still people who are not taking proper precautions and maintaining social-distancing while going out for emergency situations. To ensure this the government is trying to monitor the actions of each and every citizen. It is not realistically possible to monitor the actions of each and every citizen. This problem can be possibly solved by the use of machine learning. In this paper we have presented face detection based technique to combat with COVID-19. At first this technique captures the image and detects the face using viola zones method. Then the detected face images are classified to detect the mask. It actually detects whether every person have used mask or not. If there present anyone without mask then the system starts alarm to inform it to the responsible authority and to alert the public. Here Principal Component Analysis (PCA) has been used to extract the feature from the detected face images and for the detection of mask K-Nearest Neighbor (KNN) classifier has been used.
Indoor Future Person Localization from an Egocentric Wearable Camera

Accurate prediction of future person location and movement trajectory from an egocentric wearable camera can benefit a wide range of applications, such as assisting visually impaired people in navigation, and the development of mobility assistance for people with disability. In this work, a new egocentric dataset was constructed using a wearable camera, with 8,250 short clips of a targeted person either walking 1) toward, 2) away, or 3) across the camera wearer in indoor environments, or 4) staying still in the scene, and 13,817 person bounding boxes were manually labelled. Apart from the bounding boxes, the dataset also contains the estimated pose of the targeted person as well as the IMU signal of the wearable camera at each time point. An LSTM-based encoder-decoder framework was designed to predict the future location and movement trajectory of the targeted person in this egocentric setting. Extensive experiments have been conducted on the new dataset, and have shown that the proposed method is able to reliably and better predict future person location and trajectory in egocentric videos captured by the wearable camera compared to three baselines.
Performance Comparison of Spoken Language Detection Models with Embedding Replacement

Deep learning-based abuse detection models have many limitations in improving detection accuracy due to frequent typos and spacing errors in Korean text. Particularly, in the process of morphological analysis of spoken language for generating learning data, there is a problem that unnecessary morphemes are frequently extracted, making it difficult to grasp the meaning of words. This is the biggest cause of degrading the accuracy of the abuse detection model. In this paper, to overcome this problem of Korean spoken language, we design and implement detection models based on embedding, and compare the accuracy of abuse detection. We use three embedding models: fastText, SKT-KoBERT, and KoELECTRA for detection; we then compare and evaluate the performance of each embedding-based abuse detection model through various experiments. As a result of the experiments, the comparison of the ambiguity determination, SKT-KoBERT showed significantly higher performance than fastText. The comparison according to the pre-training method also showed higher performance of SKT-KoBERT than KoELECTRA. Through the experimental results, we believe that more effective embedding technology can be applied to various spoken language-based deep learning services.
FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.
Sentence Level Language Identification in Gujarati-Hindi Code-Mixed Scripts

On social media, people often use to chat in multilingual code. It happens when a person knows more than one language. Multilingual speakers often use multiple languages to communicate when speaking and writing. This language interchanging involves complex grammar, and the terms used for these are code-mixing and code-switching. Due to the increasing social media usage and conversation on online chat platforms, many textual and non-textual communication happens in different languages. As a result, a large number of multilingual code-mixed corpus generates in digital form. To apply any Natural Language Processing (NLP) techniques on such types of corpus to build an Information Retrieval (IR) system, which may not give desired outcomes because of the noisy nature of inputs. This type of noise in the scenario of multiple languages in a multilingual code-mixed corpus and identifying particular language at the sentence level is problematic because it involves various forms of a single language. i.e., code-mixed sentences involve different languages and are written in the Roman script and the native script. In this paper, we address the solution to the language identification problem in the Gujarati code-mixed script in which the Gujarati language is written in various forms, i.e., romanized and non-romanized forms. In the context of the current problem, we first time created our own dataset and for experiment we have used a large dataset for the Gujarati language in which Gujarati mixed-up with Hindi and English. Further, we have used various machine learning classifiers. We have first time used this much large amount of data of Gujarati and received the highest accuracy 92% with a Support Vector Classifier.
An Evaluation of Value-Oriented Review for Software Requirements Specification

A software requirements specification (SRS) is a detailed description of a software system to be developed. This paper proposes and evaluates a lightweight review approach called value-oriented review (VOR) to detect defects ... | Find, read and cite all the research you need on Tech Science Press
Contextual contracts for component‐oriented resource abstraction in a cloud of high performance computing services

Summary Efforts to support high performance computing (HPC) applications' requirements in the context of cloud computing have motivated us to design HPC Shelf , a cloud computing services platform to build and deploy large‐scale parallel computing systems. We introduce Alite , the contextual contract system of HPC Shelf , to select component implementations according to requirements of the host application, target parallel computing platform characteristics (e.g., clusters and MPPs), quality of service (QoS) properties, and cost restrictions. It is evaluated through a small‐scale case study employing two complementary component‐based frameworks. The first one aims to represent components that implement linear algebra computations based on the BLAS interface. In turn, the second one aims to represent parallel computing platforms on the IaaS cloud offered by Amazon EC2 Service.
Accurate Detection and Diagnosis of Breast Cancer Using Scaled Conjugate Gradient Back Propagation Algorithm and Advanced Deep Learning Techniques

Abstract PurposeDevelopment of breast cancer detection and its usage by different health care industries in their diagnostic center is a very much serious task for classifying cancer cells based on its specific characteristics. As a consequence, the classification process of the cancer becomes incredibly complicated for the potential users because they have a large set of attributes and parameters of the cancer cells which are available at their disposal in laboratory for diagnosis. Moreover, the proposed work gives the efficient decision for the classification of the cancer cells to diagnose the patients at there earlier stage of breast cancer. Design Methodology/approach: In this chapter, it has been proposed a layered neural network model which uses this back propagation algorithm along with scaled conjugate gradient for optimized way of classification of cancer cells by considering the appropriate parameters. Findings: The classification of cancer cells is evaluated using the proposed algorithm by designing a layered neural network model. For training the model, 70% of instances are used, for verification, 15% instances and for testing, 15% instances are used of 699 samples. After successful training of the model, the model classifies the cancers as benign (2) or malignant (4). Originality/value: The proposed methodology is an original scientific work and the algorithm used is an efficient algorithm for the classification of cancer cells. In this work, eleven data attributes are used for the classification from cancer data set.
Resource-Efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization

This paper introduces neural architecture search (NAS) for the automatic discovery of small models for keyword spotting (KWS) in limited resource environments. We employ a differentiable NAS approach to optimize the structure of convolutional neural networks (CNNs) to maximize the classification accuracy while minimizing the number of operations per inference. Using NAS only, we were able to obtain a highly efficient model with 95.4% accuracy on the Google speech commands dataset with 494.8 kB of memory usage and 19.6 million operations. Additionally, weight quantization is used to reduce the memory consumption even further. We show that weight quantization to low bit-widths (e.g. 1 bit) can be used without substantial loss in accuracy. By increasing the number of input features from 10 MFCC to 20 MFCC we were able to increase the accuracy to 96.3% at 340.1 kB of memory usage and 27.1 million operations.
Fostering Industry-Academia Collaboration in Software Engineering using Action Research: A Case Study

Background: Studies involving industry-academia collaboration (IAC) have been growing in recent years. On the one hand, scientific research is encouraged to solve real problems and, on the other, the sector in search of promoting innovation in its field. However, IAC support is still a challenging activity for Software Engineering (SE). In recent studies, the Action Research (AR) method has shown promising results in IAC projects. However, more research still needs to be done to verify the effects of applying this method. Goal: In this article, we investigate the perceptions of postgraduate students from an academic master’s and doctoral program in Computer Engineering at a Brazilian University, about the benefits and challenges faced with the application of the AR method as a strategy to promote IAC. Method: The case study was carried out with three teams of students conducting an AR in three software development companies (two private and one public). Results: As for the degree of satisfaction with the course, results revealed that 86% of the students were satisfied with the course. As for the Perception of learning with the use of AR, 100% of the students agreed that the AR method contributed positively to your learning. Conclusion: Finally, we conclude that conducting IAC projects using the AR method was a satisfactory, even though this task has been extremely challenging for all students. We observed that the reasons for this were, first, the fact that students have little experience with research methods directed to Empirical Software Engineering; second, due to the resistance faced in the industry to apply the AR method.
A Survey on Computer Ethics w.r.t. Artificial Intelligence Robot Weaponry Fuzzy Systems Autonomous Vehicles

Computer Ethics study has reached a point where Artificial Intelligence, Robot, Fuzzy Systems, Autonomous Vehicles and Autonomous Weapon Systems ethics are implemented in order to make a machine work without intervening and harming others. This survey presents many previous works in this field of computer ethics with respect to artificial intelligence, robot weaponry, fuzzy systems and autonomous vehicles. The paper discusses the different ethics and scenarios up through the current technological advancements and summarizes the advantages and disadvantages of the different ethics and needs of morality. It is observed that all ethics are equally important today, but human control and responsibility matters. Most recent technology can be implemented or improved by careful observation and involvement of organizations like the United Nations, International Committee for Robot Arms Control, Geneva Conventions and so on.
A Multi-Layer Data Encryption and Decryption Mechanism Employing Cryptography and Steganography

Information protection is a significant matter for both oneself and companies involved in one manner of communication or the other, particularly in cyberspace, due to the development of digital computing and communication. As a result, Steganography and Cryptography have become some of the most complementary procedures in data protection. In this modern technology era, individually employing Cryptography or Steganography will not afford the expected security from intruders. Our proposed method employs a unification of Steganography and Cryptography, which utilizes the Blowfish algorithm, the Advanced Encryption Standard algorithm and remarkable features of the residue numbering system, the Least Significant Bit algorithm and the operators of Genetic Algorithm. The outcomes show that more than 70% minimal error is reduced (for stego images) compared to the previous methods in terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). This approach above is less complicated concerning runtime and consumes less power. Higher NPCR/UACI value in the result indicates an increased level of security.
Deployment of a Machine Learning System for Predicting Lawsuits Against Power Companies: Lessons Learned from an Agile Testing Experience for Improving Software Quality

The advances in Machine Learning (ML) require software organizations to evolve their development processes in order to improve the quality of ML systems. Within the software development process, the testing stage of an ML system is more critical, considering that it is necessary to add data validation, trained model quality evaluation, and model validation to traditional unit, integration tests and system tests. In this paper, we focus on reporting the lessons learned of using model testing and exploratory testing within the context of the agile development process of an ML system that predicts lawsuits proneness in energy supply companies. Through the development of the project, the SCRUM agile methodology was applied and activities related to the development of the ML model and the development of the end-user application were defined. After the testing process of the ML model, we managed to achieve 93.89 accuracy; 95.58 specificity; 88.84 sensitivity; and 87.09 precision. Furthermore, we focused on the quality of use of the application embedding the ML model, by carrying out exploratory testing. As a result, through several iterations, different types of defects were identified and corrected. Our lessons learned support software engineers willing to develop ML systems that consider both the ML model and the end-user application.
WITHDRAWN: Encryption methodology on crypto keys from image using chaos logistic maps

Image based cryptography is the new area in the information technology environment, which is used in development of securing information algorithms. Most of the proposed crypto methods depend on the key in their procedures. Therefore, a key has its own identity in the strength of any crypto algorithm. Hence, the proposal brings a concept for generation of symmetric key using chaos logistic map and images. In addition, adds on with protection of the symmetric key by key encryption. The approach begins by selecting a feature of an image, thereby selecting defined number of points which are applied to chaos logistic map resulting to symmetric key generation. The resultant key is protected by using a masking methodology. Statistical tests are performed on the generated key for its sensitivity and characteristics which proves that it is an efficient way to generate key from image using chaos logistic maps.
Standardizing Sinhala Code-Mixed Text using Dictionary based Approach

Code-mixing is one of the biggest challenges when processing social media text. This paper presents a thorough review on the state of the art code-mixed text processing and identified the main challenges in processing Sinhala code-mixed text. In this study we could identify how researchers conducted different kinds of tasks such as normalization of code-mixed data, word level language identification of the code-mixed text etc. The study lead to identify the challenges in Sinhala code-mixed text such as phonetic transliterations, borrowing of words, spelling errors, embedded languages, the use of numeric characters in words, discourse marker switching etc. Based on this challenges identified, it was necessary to standardize the Singlish text to Sinhala letters, since there are so many representations for the same word. So a dictionary is proposed where Sinhala letters are mapped to Singlish text which could be used as a standardization. Finally the paper discuss about the future work planed on using the proposed dictionary for Sinhala code-mixed data analysis.
Extended reality interactive wall: User experience design research-creation

This article presents the development of an extended reality (XR) interactive wall design that employs augmented reality (AR) and virtual reality (AR) to display digital content in educational and exhibition settings. A wall design printed with a number of conventional graphical elements was enhanced with AR and VR, including AR-based video, a 360 virtual environment and 3D-scanned materials. The early design of the XR interactive wall was tested several times in a university setting during academic events designed to demonstrate science, technology, engineering and mathematics (STEM)-based content. This provided AR and VR experiences to participating visitors in an exhibition setting, where multimedia mobile devices were provided to aid these experiences. Inclusive design principles were employed, and elements integrated that allowed users (standing adults, children, wheelchair users) to view and interact with AR and VR content conveniently. After the initial introduction of the interactive wall design, the prototype was redesigned to fit both handheld and head-mount-device (HMD)-based mobile configurations. In this article we describe the prototype, a user experience study based on laboratory testing and directions for future work.