Reviewerly/exHarmony huggingface dataset:
PRODUCT REQUIREMENTS SPECIFICATION PROCESS IN PRODUCT DEVELOPMENT

Abstract Requirements engineering and requirements management are essential sub-processes of product development and are an integrated part of virtually all product development models and industrial process descriptions. Proprietary and context specific processes for working with requirements are used in industrial design practice. However, these are not appropriately reflected in existing process models for product development. Existing standards describe the content and generation of requirements documents but not their integration in the product development process. The study is based on a retrospective analysis of a set of representative real-world product development projects from automotive industry and rail industry. Comparing the processes downstream the milestone “release of PRD”, it was found that subsequent processes to manage requirements and specifications do not differ much with regard to industrial context. Based on this, a model for the product requirements specification (PRS) process is proposed which addresses the gap.
Multi-Depth Adaptive Networks For Wireless Interference Identification

Wireless interference identification (WII) is a promising technology for non-cooperative communication systems in both civilian and military scenarios. With the rapid development of artificial intelligence, deep learning (DL) based WII methods have been proposed. However, the existing networks based on DL do not have the adaptive ability, and this situation may cause a waste of computing resources. In this paper, we propose a novel Multi-Depth Adaptive Network (MDANet), and it can adaptively determine the forward propagation depth according to the difficulty of input samples in inference. Specifically, we firstly divide a given convolutional neural network (CNN) into several blocks, and each block has its own output of classification by attaching a fully connected layer. The mechanism of confidence is introduced to enable the network to dynamically select depth of forward propagation and allocate appropriate computational resources depending on the complexity of samples during test time. In addition, we improve the recognition ability of early blocks through three proposed approaches. Experiments demonstrate that the MDANet can reduce the calculation cost and prediction time significantly without the performance loss.
Rider Chaotic Biography Optimization-driven Deep Stacked Auto-encoder for Big Data Classification Using Spark Architecture

The novelty in big data is rising day-by-day in such a way that the existing software tools face difficulty in supervision of big data. Furthermore, the rate of the imbalanced data in the huge datasets is a key constraint to the research industry. Thus, this paper proposes a novel technique for handling the big data using Spark framework. The proposed technique undergoes two steps for classifying the big data, which involves feature selection and classification, which is performed in the initial nodes of Spark architecture. The proposed optimization algorithm is named rider chaotic biography optimization (RCBO) algorithm, which is the integration of the rider optimization algorithm (ROA) and the standard chaotic biogeography-based optimisation (CBBO). The proposed RCBO deep-stacked auto-encoder using Spark framework effectively handles the big data for attaining effective big data classification. Here, the proposed RCBO is employed for selecting suitable features from the massive dataset.
Bridging the Distribution Gap of Visible-Infrared Person Re-identification with Modality Batch Normalization

Visible-infrared cross-modality person re-identification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Most existing works integrate batch normalization layers into their neural network, but we found out that batch normalization layers would lead to two types of distribution gap: 1) inter-mini-batch distribution gap- the distribution gap of the same modality between each mini-batch; 2) intra-mini-batch modality distribution gap-the distribution gap of different modality within the same mini-batch. To address these problems, we propose a new batch normalization layer called Modality Batch Normalization (MBN), which normalizes each modality sub-mini-batch respectively instead of the whole mini-batch, and can reduce these distribution gap significantly. Extensive experiments show that our MBN is able to boost the performance of VI-ReID models, even with different datasets, backbones and losses.
An Overview of Quantum Security for Distributed Energy Resources

Quantum computing is a game-changing technology that will push the boundaries of cybersecurity and cryptography of power infrastructures including distributed energy resources (DER). This paper explores new security vulnerabilities caused by potential quantum computing attacks and potential defense strategies using quantum key distribution (QKD) and post-quantum cryptography (PQC) in DER network systems. This paper will be used as a cybersecurity baseline reference toward a quantum-safe DER system.
Evolving Simple Solutions to the CIFAR-10 Benchmark using Tangled Program Graphs

The goal of the CIFAR-10 benchmark is recast from the perspective of discovering light-weight as well as accurate solutions. Specifically, the image data, on which CIFAR-10 is based, requires multiple practical issues to be addressed that are not often considered collectively when applying genetic programming to classification problems. Issues of particular interest include cardinality, multi-class classification and diversity maintenance. We demonstrate that diversity maintenance and cardinality can be approached simultaneously by adopting a data subset to compose pools of exemplars for lexicase selection. The issues of multi-class classification and solution simplicity are addressed by adopting the tangled program graph (TPG) approach to emergent modularity. In addition, the mutation operator is modified to ensure that class labels do not `die out' during evolution. The resulting benchmarking study demonstrates solutions that are significantly more accurate than AutoML while providing comparable accuracies with solutions from unsupervised feature discovery, i.e. 70% accuracy. However, unlike the latter TPG solutions are several orders of magnitude simpler.
Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">◦</sup> and 7.44 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">◦</sup> error on MPI-IGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.
Development of Personas to Communicate Narrative-Based Information About the HPV Vaccine on Twitter

Introduction: Personas are based on real-life typologies of people that can be used to create characters and messages to communicate important health information through relatable narrative storylines. Persona development is data-driven and can involve multiple phases of formative research and evaluation; however, personas are largely underutilized in digital health research. The purpose of this study was to create and document persona development to deliver narrative-focused health education for parents on Twitter with the goal of increasing uptake of HPV vaccination among adolescents. Methods: Leveraging data from a mixed-method study conducted in the U.S. with a diverse population of parents with adolescents ages 9-14, we used both qualitative and quantitative data (e.g., the National Immunization Survey-Teen, focus groups, and social media) to create personas. These data sources were used to identify and develop key characteristics for personas to reflect a range of parents and their diverse understandings and experiences related to HPV vaccination. A parent advisory board provided insight and helped refine persona development. Results: Four personas emerged and were characterized as the (1) Informed Altruist, (2) Real Talker, (3) Information Gatherer, and (4) Supporter. Characteristics differed across personas and provided insights into targeted narrative strategies. Described attributes included demographics, psychographics, communication style, vaccine goals and aspirations, vaccine challenges and frustrations, and vaccine hesitancy. Discussion: This work demonstrates how multiple data sources can be used to create personas to deliver social media messages that can address the diverse preferences and needs of parents for HPV vaccine information. With increasing usage of social media for health information among parents, it is important for researchers to consider marketing and design thinking to create health communication messages that resonate with audiences.
Fuzzy-Logic-Based Privacy-Aware Dynamic Release of IoT-Enabled Healthcare Data

The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this article, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.
The Robot is Present: Creative Approaches for Artistic Expression With Robots

There is growing interest in developing creative applications for robots, specifically robots that provide entertainment, companionship, or motivation. Identifying the hallmarks of human creativity and discerning how these processes might be replicated or assisted by robots remain open questions. Transdisciplinary collaborations between artists and engineers can offer insights into how robots might foster creativity for human artists and open up new pathways for designing interactive systems. This paper presents an exploratory research project centered on drawing with robots. Using an arts-led, practice-based methodology, we developed custom hardware and software tools to support collaborative drawing with an industrial robot. A team of artists and engineers collaborated over a 6-month period to investigate the creative potential of collaborative drawing with a robot. The exploratory project focused on identifying creative and collaborative processes in the visual arts, and later on developing tools and features that would allow robots to participate meaningfully in these processes. The outcomes include a custom interface for controlling and programming robot motion (EMCAR) and custom tools for replicating experimental techniques used in visual art. We report on the artistic and technical outcomes and identify key features of process-led (as opposed to outcome-led) approaches for designing collaborative and creative systems. We also consider the value of embodied and tangible interaction for artists working collaboratively with computational systems. Transdisciplinary research can help researchers uncover new approaches for designing interfaces for interacting with machines.
Infiltrating security into development: exploring the world’s largest software security study

Recent years have seen rapid increases in cybercrime. The use of effective software security activities plays an important part in preventing the harm involved. Objective research on industry use of software security practices is needed to help development teams, academic researchers, and educators to focus their activities.
Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a certain data-generating distribution. In the fault-free case, the D-SGD method allows all the agents to learn a mathematical model best fitting the data collectively sampled by all agents. We consider the case when a fraction of agents may be Byzantine faulty. Such faulty agents may not follow a prescribed algorithm correctly, and may render traditional D-SGD method ineffective by sharing arbitrary incorrect stochastic gradients. We propose a norm-based gradient-filter, named comparative gradient elimination (CGE), that robustifies the D-SGD method against Byzantine agents. We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters. We empirically show, by simulating distributed learning on neural networks, that the fault-tolerance of CGE is comparable to that of existing gradient-filters. We also empirically show that exponential averaging of stochastic gradients improves the fault-tolerance of a generic gradient-filter.
Intelligent Face Detection and Recognition System

Face detection applications currently can use machine learning and equations for better identification of human faces in an image or video. In several types of applications such as recognition of face application that is processing in method of biometric for checking the human’s validation which is related to facial information and different types of samples utilizing face detection system. This paper of presentation shows that system of detection and recognition in face by Viola-Jones process add together Haar Cascade and Principal Component Analysis (PCA) of technique which is proposed for gaining better accuracy level in detection of facial portions and recognition of faces. The findings show that the proposed system has performed better compared with other conventional face detection and recognition systems. The investigation has been done in a MATLAB environment on real images of the Faces94, Faces95, Faces96, and grimace databases.
Parallel Scale-wise Attention Network for Effective Scene Text Recognition

The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases.
Blockchain Platforms and Interpreting the Effects of Bitcoin Pricing on Cryptocurrencies

Blockchain technology is one of the most emerging technologies, giving trust to the users. It is a revolution to meet the demands of the users and control the supply. There are various platforms available for the implementation of blockchain technology. This paper focuses on most prevalent five blockchain platforms—Ethereum, NEO, Cardano-Ada, EOS and TRON. It presents comparison on the technical features of five platforms. Comparison has also been performed on features such as founder, founded, purpose, kind of data stored, language used for implementation, native currency, block release time, transaction rate and consensus mechanism features. Cryptocurrency is the most widespread use case of blockchain technology. Karl Pearson correlation coefficient has been calculated between Bitcoin and other five cyptocurrencies of the given platforms (Ether, NEO, Cardano-Ada, EOS and TRX), which is helpful for interpreting the effects of Bitcoin pricing on cryptocurrencies and can be highly significant for making investments in cryptocurrency.
RPI-SURF: A Feature Descriptor for Bubble Velocity Measurement in Froth Flotation With Relative Position Information

The local feature-based method is the most popular choice for froth velocity measurement. However, due to similar patterns or objects in the froth image pair, current local feature-based methods only consider local intensity character (LIC) and cannot extract the matches well. Therefore, we propose a new feature descriptor named relative position information (RPI)-SURF that integrates the LIC and the RPI from the nearest reference point. First, we use the SURF descriptor with a distance constraint to extract the LIC. Next, we calculate the RPI by the direction and distance to the nearest reference point pair. After that, we integrate the LIC and RPI to demonstrate the new feature descriptor. Extensive experiments verify the effectiveness of the proposed feature descriptor. Compared with the SURF descriptor, the recall and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of the proposed RPI-SURF descriptor have been obviously increased, and the number of detected accurate matches on real froth image pairs by the RPI-SURF descriptor has been increased by at least nearly 60%.
Application of a Deep-Learning Technique to Non-Linear Images From Human Tissue Biopsies for Shedding New Light on Breast Cancer Diagnosis

The development of label-free non-destructive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II, and III. By the implementation of a convolutional neural network (CNN) model, the differentiation of malignant from benign breast tissue samples and the discrimination of the different grades of cancer in a fast and accurate way were achieved. The obtained results provide a step ahead towards the use of optical diagnostic tools in conjunction with the CNN image classifier for the reliable and rapid malignancy diagnosis in clinic.
Fine-Grained Secure Attribute-Based Encryption

Fine-grained cryptography is constructing cryptosystems in a setting where an adversary's resource is a-prior bounded and an honest party has less resource than an adversary. Currently, only simple form of encryption schemes, such as secret-key and public-key encryption, are constructed in this setting. In this paper, we enrich the available tools in fine-grained cryptography by proposing the first fine-grained secure attribute-based encryption (ABE) scheme. Our construction is adaptively secure under the widely accepted worst-case assumption, $$\mathsf {NC^1}\subsetneq \mathsf{\oplus L/poly}$$ , and it is presented in a generic manner using the notion of predicate encodings (Wee, TCC'14). By properly instantiating the underlying encoding, we can obtain different types of ABE schemes, including identity-based encryption. Previously, all of these schemes were unknown in fine-grained cryptography. Our main technical contribution is constructing ABE schemes without using pairing or the Diffie-Hellman assumption. Hence, our results show that, even if one-way functions do not exist, we still have ABE schemes with meaningful security. For more application of our techniques, we construct an efficient (quasi-adaptive) non-interactive zero-knowledge (QA-NIZK) proof system.
A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks

People connected by huge-size socialnetworks are highly dependent on recommendation systems to discover interesting persons, contents, and commodities. Extracting user interests and modeling their evolutions from user historical behavior are vital for algorithms to judge whether users are interested in given persons and items. The existing methods have two shortcomings: firstly, they do not have the high-order ability to process temporal sequences, which leads to their inability to mine the evolution of user interests; secondly, the models take full large-scale information of items as input, which causes a severe problem of overfitting. Thus, we propose a new hybrid neural network, DGRU, which integrates Factorization-Machine Based Neural Network (DeepFM) and Gated Recurrent Unit Neural Network (GRU). The DeepFM component is responsible for performing the autonomic feature combination, and the GRU component is designed to model user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors. It contains information on what users like and dislike. Moreover, the conciseness of the format is helpful to avoid the problem of overfitting. Experiments on three real datasets demonstrated that the proposed model has a better CTR prediction performance and robustness than the state-of-the-art models.