Reviewerly/exHarmony huggingface dataset:
Dysfunctional Agile–Stage-Gate Hybrid Development: Keeping Up Appearances

Increasingly, the development of today’s “smart” products requires the integration of both software and hardware in embedded systems. To develop these, hardware firms typically enlist the expertise of software development firms to offer integrated solutions. While hardware firms often work according to a plan-driven approach, software development firms draw on Agile development methods. Interestingly, empirically little is known about the implications and consequences of working according to contrasting development methods in a collaborative project. In response to this research gap, we conducted a process study of a collaborative development project involving a software firm and a hardware firm, within which the two firms worked according to contrasting development methods. We found that the software firm was gradually compelled to forgo its Agile method, creating a role conflict in terms of its way of working. As such, our results contribute to the literature on Agile–Stage-Gate hybrids by demonstrating how, in collaborative embedded systems development, hybridization of development methods may cause projects to fail. Our main practical implication entails the introduction of the “sequential Agile approach.”
Phishing—A Cyber Fraud: The Types, Implications and Governance

Internet users are becoming ignorant with their data and the transparency of information due to the nature of high-speed internet today. Regrettably, internet users are deceived by engineering tactics performed by highly trained people, namely cybercriminals. Thus, in order to combat phishing attacks, internet users should be educated on security concerns, the influence of social engineering and anti-phishing knowledge. This paper presents a literature review of phishing, a type of cyber fraud, covering the types of phishing, the implications and governance. This study benefits the public to mitigate phishing attacks and increase phishing awareness.
Application of New Technology in Education: Design and Implementation of Graduate Certificate Model Based on Intelligent Graph Element Technology

Modern information technology empowers education and infuses new vigor into educational innovation. However, current university diplomas are old-fashioned, not connected to information technology, and easy to copy. Two-dimensional code technology, widely popular in China because of its intuitive, simple, convenient, and other advantages, should be integrated into the field of education. Two-dimensional code technology is the advanced form of graphic code technology. However, through the analysis of the current situation of graphic code technology research and education applications including two-dimensional code, there are no actual cases of graphic code technology applied to graduation certificates, leaving a blank field of research. This is mainly due to the defects and limitations of the existing graphic code technology, so a new intelligent graph element technology (IGET) is proposed here. Intelligent graph element technology integrates the functional advantages of two-dimensional code and overcomes the issues of certificates being easy to forge and difficult to verify, by adding intuitive, discernible, and valuable new functions, suitable for the functional requirements of the graduation certificate. Based on intelligent graph element technology, a novel graduation certificate management system model is constructed here, and a graduation certificate is designed and implemented. Compared with the traditional graduation certificate, the graduation certificate based on intelligent graph elements has the advantages of being intuitive and readable, and having information security, a large amount of information, and intelligent anti-counterfeiting, verification, and traceability mechanisms. The application of intelligent graph element technology in the design and research of college graduation certificates opens a new window of digitization and informatization innovation for traditional college graduation certificates, and provides reference for other innovative applications of intelligent graph element technology in the field of education.
A Feature Selection Method of the Island Algorithm Based on Gaussian Mutation

With the development of the Internet, the data we are dealing with is becoming more and more complex, which brings about various difficulties and problems when we process and use data. Feature selection methods are able to filter and remove redundant features, which is necessary to reduce the dimension of complex data. In this paper, the island algorithm is used to find the optimal feature subset in the set of feature subsets, but as the number of iterations increasing, the island algorithm tends to local optimization. To address this problem, a Gaussian mutation strategy is introduced to improve the island algorithm and proposed an island algorithm based on Gaussian mutation (IAGM). The main idea of the IAGM algorithm is to set a warning sign which aims to record the global optimum value generated by each iteration and judge whether the changes are less than a threshold value in the optimum value for three consecutive iterations. If it is less than the global optimum, the Gaussian mutation is applied to the current globally optimal plant location and then evaluates the new location and exchanges them when it is better than the global optimum. Otherwise, discard this plant and add a Gaussian mutation to the formula of the next new plant towards the global optimum, which is used to diversify the populations. Meanwhile, combining the IAGM algorithm with a support vector machine classifier, a feature selection method of the island algorithm based on Gaussian mutation (IAGMFS) is proposed. The UCI dataset is selected for simulation experiments and applied to classify images, which represents the advantages of the feature selection in IAGMFS proposed in this paper.
Dynamic service composition framework for service‐oriented architectures based e‐government in Cameroon

Abstract E‐government has become a strategic tool for managing public administration. It makes it possible to improve interoperability between government's computer systems agencies. Taking profit to technological advances, notably the internet and web services, it has been associated with service‐oriented architectures (SOA). Service‐oriented e‐government has thus grown rapidly in developed countries. He has also gradually established itself in many projects in developing countries to take advantage to the economic benefits derived from SOA. However, when the activity of stakeholders becomes important in an e‐government based on SOA, the scalability becomes a major challenge. This is even more striking in developing countries, which suffer from less robust infrastructures and poor energy supply. The scalability has its origins mainly in dynamic service composition operations. This is why this work proposes to improve the dynamic service composition layer to deal with the scalability in case of service‐oriented e‐government in developing countries. This makes it possible to construct and dynamically enrich each system with a service composition server, a registry and a library of requests associated with their composite services. Thus, each system checks its ability to process a request locally before sending it to a higher‐level system. The paper demonstrates that using this participatory approach, the latency of the majority of end‐user requests is significantly shortened. Beyond this important aspect, our approach can provide with a good solution for Cameroon suffer from infrastructural insufficiency and energy instability. The evaluation of this architecture gave us satisfactory results.
Improved DBSCAN algorithm based on relative mass of the data field

The DBSCAN algorithm can discover clusters of arbitrary shapes, but it has difficulty in predicting the appropriate clustering parameters. In this study, the data field is introduced into the number field space, and the relative mass (RM) calculation method of the data field is proposed, and the first N points with larger mass in the dataset are calculated as the initial points of clustering by the RM algorithm. Then the optimized influence factor sigma is used to calculate the force range radius to achieve the optimization of the field radius parameter, so as to select the appropriate clustering parameters. In addition, this study improves the efficiency of computing large datasets by implementing the improved algorithm for parallel computing in a distributed cluster. Finally, the effectiveness of the improved algorithm is verified on three publicly available datasets, and the efficiency of parallel computation is verified on three large datasets. The results show that (1) the improved DBSCAN algorithm can effectively solve the problem of difficult selection of clustering parameters. (2) The maximum speedup ratio of parallel computation reaches 2.12 when the size of the large dataset is increased from 30,000 to 150,000 and the number of nodes involved in the computation is increased from one to five, and the average operation efficiency of the improved algorithm is improved by 32.45% compared with the original algorithm.
An interpretive study of stakeholders’ privacy issues in blockchain: a healthcare context

Ever-growing and rapidly changing healthcare information systems (HIS) encourage many new technologies to integrate with it to enrich the treatment and facilitate patients. Blockchain, a decentralised digital ledger, is legitimately disrupting traditional HIS due to its characteristics such as immutability, interoperability, decentralise, and security. Blockchain’ applications in healthcare industries are attracting investors and organisations to develop platforms for future. However, security and privacy concerns hinder blockchain adoption in the health sector. Therefore, there is a need to develop deeper understandings about these issues and require strategies to address these issues so that the desired values can be obtained. Besides, privacy could mean different to different people such as patients, doctors, and admin staff. Therefore, there is a need to explore it from various stakeholder perspectives too. Using interpretive qualitative research approach, this research-in-progress will extend the body of knowledge by scrutinising the stakeholders’ perception of privacy concerns and its relationship in blockchain based HIS. The findings of this study will contribute to address privacy issues emerged from the research and help to eliminate them before implementing blockchain. This paper supplies an appropriate research approach for multidimensional research in healthcare. In addition, this research-in progress will formulate a framework which provide awareness to stakeholders about privacy issues when they use blockchain based HIS in future.
A multitask deep representation for Gleason score classification to support grade annotations

The Gleason grade system is the main standard to quantify the aggressiveness and progression of prostate cancer. Currently, exists a high disagreement among experts in the diagnosis and stratification of this disease. Deep learning models have emerged as an alternative to classify and support experts automatically. However, these models are limited to learn a rigid stratification rule that can be biased during training to a specific observer. Therefore, this work introduces an embedding representation that integrates an auxiliary task learning to deal with the high inter and intra appearance of the Gleason system. The proposed strategy implements as a main task a triplet loss scheme that builds a feature embedding space with respect to batches of positive and negative histological training patches. As an auxiliary task is added a cross-entropy that helps with inter-class variability of samples while adding robust representations to the main task. The proposed approach shows promising results achieving an average accuracy of 66% and 64%, for two experts without statistical difference. Additionally, reach and average accuracy of 73% in patches where both pathologists are agree, showing the robustness patterns learning from the approach.
Thermal infrared face detection method based on deep learning

With the development of artificial intelligence and deep learning, visible face detection and recognition have always been a research hotspot. However, most visible face detection systems cannot detect and recognize faces without external illumination. Thermal infrared technology is the use of any object higher than absolute zero can emit different wavelengths of electromagnetic radiation, the use of different infrared radiation, an infrared thermal imager can transform the infrared radiation distribution of the object into an image, so it can run in the environment without light. In this paper, the real-time detection of multi-scale and multi-target in the field of view can be realized in the self-collected HUST-MIR Face Database. This method is based on a convolution neural network, which can realize fast and accurate face detection of the thermal infrared image.
Universal Convolutional Neural Network for Histology-Independent Analysis of Collagen Fiber Organization in Scar Tissue

Histological examination of collagen fiber organization is essential for pathologists to observe the wound healing process.A convolutional neural network (CNN) can be utilized to visually analyze collagen fibers during tissue remodeling in histology images.In this study, a universal CNN (UCNN) independent of the histological staining process is proposed to classify the histology images of burn-induced scar tissues and characterize collagen fiber organization.Normal and scar tissues obtained from an in vivo rodent model are stained using Masson's Trichrome (MT) and Hematoxylin & Eosin (H&E).The proposed universal model is trained using both MT-and H&E-stained histological image datasets over multiple scales with color augmentation, and classification accuracies of up to 98% and 97% are achieved for the MT-and H&E-stained image datasets, respectively.Regardless of the histological staining process used, the collagen characteristics are visualized by determining the density and directional variance of the normal and scar tissues by using the features extracted with the proposed universal model.Statistical analysis results demonstrated clear differences between scar and normal tissues in terms of collagen fiber organization.The proposed UCNN model can contribute to the development of an intelligent and efficient method that pathologists can use to rapidly evaluate wound healing and tissue remodeling.
SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing imagery is a challenging task because of its inherent attributes, e.g., dense parking, small sizes, various angles, etc. Prevalent vehicle detectors adopt an oriented/rotated bounding box as a basic representation, which needs to apply a distance regression of height, width, and angles of objects. These distance-regression-based detectors suffer from two challenges: (1) the periodicity of the angle causes a discontinuity of regression values, and (2) small regression deviations may also cause objects to be missed. To this end, in this paper, we propose a new vehicle modeling strategy, i.e., regarding each vehicle-rotated bounding box as a saliency area. Based on the new representation, we propose SR-Net (saliency region representation network), which transforms the vehicle detection task into a saliency object detection task. The proposed SR-Net, running in a distance (e.g., height, width, and angle)-regression-free way, can generate more accurate detection results. Experiments show that SR-Net outperforms prevalent detectors on multiple benchmark datasets. Specifically, our model yields 52.30%, 62.44%, 68.25%, and 55.81% in terms of AP on DOTA, UCAS-AOD, DLR 3K Munich, and VEDAI, respectively.
Particle Filter with Weight Adjustment and Its Application on Heat Exchange Process with Time Delay and Large Sensor Noise

Particle filter has been popularized and used for solving estimation problems since the early 1990s. Extensive research has advanced the generic particle filter to improve the performance and applicability in various ways. A variant of the generic particle filter algorithm is shown in this paper. The particle filter variant re-adjusts the calculation method of the particle weight during the update stage, where the normalized exponential weight instead of the original absolute particle weight is used to deal with the problem of unavailability of reliable weights when encountering large realistic noises. Simulation shows the effectiveness of the method.
Analysis of Medical Image by using Machine Learning Applications of Convolutional Neural Networks

Deep learning or machine learning using image input is a good field in medical image analysis that is rapidly evolving. Machine learning is expected to become the norm in the field of medical image analysis by the inclusion of images in the next few decades. The idea of machine learning (ML), also known as deep learning, has recently become very popular in many fields, including computer vision. The in-depth learning strategy based on the Convulsive Neural Network (CNN) was launched in late 2012 and quickly won the prestigious Global Computer Vision competition, ImageNet ranking. Since then, academics from many fields, including medical film analysis, have begun to take an active part in the rapid progression of deep learning. This paper looks at in-depth learning techniques and how to use them in the analysis of medical images. This study is 1) managing customary machine learning strategies in the PC vision industry, 2) what changed in machine learning prior and then afterward the presentation of top to bottom learning, 3) dealing with in-depth learning patterns and 4) indepth practice applications for medical image analysis. Before and After Deep Learning Compare Deep Learning with Machine Learning Before Profound Learning ML With Feature Input (or Feature-Based ML) is normal and the essential and key contrast among pre-and post-profound learning ML is straightforwardly to the picture information object. Although model depth is an important asset, division or element extraction is the wellspring of top to bottom learning capacity. The graph of the Deep Learning test shows a long history of deep learning methods in the classroom for ML using inputs, with the exception of the new term “deep learning”. The term “deep learning” was used in ML class medical image analysis with image entry before it was coined to address issues such as pest and non-pest taxonomy, taxonomy, taxonomy and pest or organ identification. Pests. Deep learning using image input is a very powerful, versatile, high-performance technology that has the potential to take clinical picture investigation to a higher level and is normal to become a major in-depth technology in the coming decades.
Explainable Digital Currency Candlestick Pattern AI Learner

More and more hedge funds have integrated AI techniques into the traditional trading strategy to speculate on Digital Currency. Among the conventional technical analysis, candlestick pattern recognition is a critical financial trading technique by visual judgment on graphical price movement. A model with high accuracy still can not meet the demand under the highly regulated financial industry that requires understanding the decision-making and quantifying the potential risk. Despite the deep convolutional neural networks (CNNs) have a significant performance. Especially in a highly speculative market, blindly trusting a black-box model will incur lots of troubles. Therefore, it is necessary to incorporate explainability into a DNN-based classic trading strategy, candlestick pattern recognition. It can make an acceptable justification for traders in the Digital Currency market. The paper exposes the black box and provides two algorithms as following. The first is an Adversarial Interpreter to explore the explainability. The second is an Adversarial Generator to enhance the model&#x0027;s explainability. To trust in the AI model and understand its judgment, the participant adopts powerful AI techniques to create more possibilities for AI in the Digital Currency market.
Evaluating the content accuracy of augmented reality applications on the Solar System

Abstract Advancements in mobile technologies have accelerated mobile Augmented Reality (AR), and many applications have been developed and presented in online stores. This study aims to evaluate the accuracy of the AR model of the Solar System simulated in mobile AR applications located in the Google Play Store. The applications were examined in the store between 1 December 2020 and 1 January 2021. A criterion was developed and 23 applications were determined that met the criterion. The mobile AR applications were coded according to a coding scheme devoted to evaluating the content requirements of the Solar System. The results showed that the applications do not meet the requirements of the model of the Solar System. In addition, the applications are insufficient to accommodate the gravity, inertia, scale, and system formation that are the requirements of the teaching of the Solar System as a whole. The results also revealed that the applications have the potential to lead to similar misconceptions as mentioned in the literature. The results suggest that the applications should be developed or reviewed by specialists or teachers before using in science classrooms. In addition, research on AR should be more focused on the quality of mobile AR applications in online stores.
Subgroup Identification and Regression Analysis of Clustered and Heterogeneous Interval-Censored Data

Clustered and heterogeneous interval-censored data occur in many fields such as medical studies. For example, in a migraine study with the Netherlands Twin Registry, the information including time to diagnosis of migraine and gender was collected for 3975 monozygotic and dizygotic twins. Since each study subject is observed only at discrete and periodic follow-up time points, the failure times of interest (i.e., the time when the individual first had a migraine) are known only to belong to certain intervals and hence are interval-censored. Furthermore, these twins come from different genetic backgrounds and may be associated with differential risks for developing migraines. For simultaneous subgroup identification and regression analysis of such data, we propose a latent Cox model where the number of subgroups is not assumed a priori but rather data-driven estimated. The nonparametric maximum likelihood method and an EM algorithm with monotone ascent property are also developed for estimating the model parameters. Simulation studies are conducted to assess the finite sample performance of the proposed estimation procedure. We further illustrate the proposed methodologies by an empirical analysis of migraine data.
Asymmetric Effects of Private Debt on Income Growth

Abstract This paper uses a panel vector autoregressive model to study the differential effects of components of private debt on income growth for a large panel of countries. While household debt growth in a given period generally has a positive impact on income, this effect is much stronger for countries with relatively lower levels of income and household debt-to-GDP ratios. On the other hand, the responsiveness of income growth to an increase in corporate debt varies across countries, with a consistently negative impact in richer and/or more heavily indebted countries. A simple extension of our framework suggests the roles of investment and consumption spending in explaining the varying effects of household and corporate debt on growth. We carry out several exercises to illustrate the robustness of our results.