Reviewerly/exHarmony huggingface dataset:
Team Behaviour Model as a Tool for Determining the Project Development Trajectory

The scientific novelty of the work consists in the fact that a model of the Scrum team’s behavior during the project life cycle has been developed, which takes into account the influence of the participants’ competence and role potential on the result of team work. Based on the Drexler and Sibbet’s team behavior model, the model 7 F’s is proposed. This model reflects 7 stages of the project team members working together: Finding, Faith, Focus, Functioning, Forcing, Fruitfulness and Forwarding. This model is presented in the form of a Markov chain, where seven stages of team behavior are presented in the form of a graph of transitions from one state to another, the probabilities of transition to other states, as well as the probabilities of maintaining current states, are indicated. The transition probabilities of the Markov chain for the seven stages of team behavior determine its characteristics that change in discrete time in steps in the form of a certain set of state probabilities. The cognitive property of a model built on the basis of the Markov chain allows you to set certain specific values for the transition probabilities of the system. Practical recommendations are given for determining the values of transition probabilities for different states of the system. It is shown that the probabilities of the transition of system states can be determined on the basis of an estimate of the time spent moving from one stage of joint teamwork to another. Two options for team behavior are being studied, i.e. the basic version of team behavior and team behavior after the implementation of the in-depth knowledge system and the adaptive knowledge generation system. The study shows the change in state probabilities through the implementation of these systems. The proposed model allows quantifying the current progress of the project processes as a result of changes in the project team and time costs caused by problems that each participant and the whole team may encounter at seven stages of teamwork. The 7 F’s model is applied at the Retrospective stage in the Scrum process in order to increase the efficiency of the Scrum team within new iterations.
Gastric Polyps Detection by Improved Faster R-CNN

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. In this work, we use an improved Faster R-CNN network to detect the gastric polyps. We use the ROI align operation to replace ROI pooling operation, use the GIoU loss to replace the original smooth L1 loss and use the soft-NMS to replace the traditional NMS in the Faster R-CNN network. The ROI align operation can solve the problem of misalignment. This GIoU loss can take the IoU between the predicted value and the ground truth value into account to the greatest extent and improve the detection performance. This GIoU loss in the detection network will effectively improve the accuracy of the box regression. The soft-NMS can reduce the deletion of bounding boxes by mistake in the post processing stage. The Faster R-CNN network not only achieves good results in general image detection, but also in gastric polyps image detection. The improved Faster R-CNN can further improve the detection performance in the gastric polyps. Compared to the other polyps detection methods, precision, recall rate and F1 score of our network has been achieved higher values. The final detection results about precision, recall rate and F1-score of our work is 78.96%, 76.07%, 77.49%. In gastric polyps detection, this method can be of great help to doctors and patients.
Trust Impact on Blockchain &amp; Bitcoin Monetary Transaction

Purpose: The purpose of this study is toanalyses the factors that influence blockchain & bitcoin for peer money transfer. Also, this study identifies the concept of blockchain and cryptocurrency. Furthermore, the conceptual framework of this study developed incorporating trust on software as a mediator for a better understanding of the use of blockchain in a peermoney transaction. Theory: The unified theory of acceptance and use of technology (UTAUT2) by adding trustin software as an independent variable. This research also demonstrates the relationship between the blockchain and bitcoininpeer transaction. The theoretical framework of this study developed to merge trust on software as a synthesis in UTAUT2 for a better understanding of the use of blockchain for peer transaction in Malaysia. Methodology: 142 self-administered questionnaires distributed through simple random sampling technology aimed at blockchain users in Malaysia from 20 to 30 years. Data analysis performed using PLS. This studyalso demonstrate the relationship of peer transaction and trustin software have a positive and significant relationship with using blockchain& bitcoin. Significant: This research will significantly contribute to blockchain diversityin Malaysia. It is also providing them with useful information about the interaction between the critical determinants of the trust and intention of blockchain behaviourin Malaysia. Originality: This is one of the most recent studies that describe the blockchain& bitcoin fundaments and transaction policy in Malaysia.
Spherical Nanoparticle Parameter Measurement Method based on Mask R-CNN Segmentation and Edge Fitting

Nanoparticle size measurement and character description are important in Nano technology. In order to accurately measure the parameter of nanoparticles and evaluate the quality of nanomaterials, a spherical nanoparticle size measurement method based on Mask R-CNN segmentation and edge fitting is proposed according to the image captured by transmission electron microscopy. Firstly, according to the characteristics of the agglomerated adhesion particles in the nanoparticle image, the Mask R-CNN network is selected to segment the nanoparticle image. Secondly, based on the particle segmentation results, the nanoparticle boundary is fitted with a circle, and an edge correction scheme is proposed for the incomplete nanoparticles. Finally, the particle size and shape parameters of the particles are measured based on the fitting results. The experimental results verify the effectiveness of the proposed method.
Online Data Center Traffic Classification Based on Inter-Flow Correlations

Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.
Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis

We propose a novel Edge guided Generative Adversarial Network (EdgeGAN) for photo-realistic image synthesis from semantic layouts. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to two largely unresolved challenges. First, the semantic labels do not provide detailed structural information, making it difficult to synthesize local details and structures. Second, the widely adopted CNN operations such as convolution, down-sampling and normalization usually cause spatial resolution loss and thus are unable to fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects). To tackle the first challenge, we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. Further, to preserve the semantic information, we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout. Extensive experiments on two challenging datasets show that the proposed EdgeGAN can generate significantly better results than state-of-the-art methods. The source code and trained models are available at this https URL.
A Systematic Analysis of Flood Ontologies: A Parametric Approach

The article identifies the core literature available on flood ontologies and presents a review on these ontologies from various perspectives like its purpose, type, design methodologies, ontologies (re)used, and also their focus on specific flood disaster phases. The study was conducted in two stages: i) literature identification, where the systematic literature review methodology was employed; and, ii) ontological review, where the parametric approach was applied. The study resulted in a set of fourteen papers discussing the flood ontology (FO). The ontological review revealed that most of the flood ontologies were task ontologies, formal, modular, and used web ontology language (OWL) for their representation. The most (re)used ontologies were SWEET, SSN, Time, and Space. METHONTOLOGY was the preferred design methodology, and for evaluation, application-based or data-based approaches were preferred. The majority of the ontologies were built around the response phase of the disaster. The unavailability of the full ontologies somewhat restricted the current study as the structural ontology metrics are missing. But the scientific community, the developers, of flood disaster management systems can refer to this work for their research to see what is available in the literature on flood ontology and the other major domains essential in building the FO.
Software Defect Prediction Model Based On KPCA-SVM

Software defect prediction technology mainly relies on machine learning algorithm to learn the measurement data of existing software. There is some redundant data in the measurement element of software defect, which will reduce the accuracy of machine learning algorithm. This paper proposes a software defect prediction model based on KPCA-SVM.First, the dimension reduction pretreatment of software defect data sets is carried out.Then, This paper using support vector machines for classification.The accuracy of the model can be improved by keeping global features in the selection of the dimension reduction algorithm.Therefore, the kernel principal component analysis (KPCA) algorithm was selected for dimensionality reduction. For the selection of classification algorithm, this paper considering that the defect prediction data set has small samples and non-linear characteristics, the support vector machine has better advantages in this kind of data set, so SVM is selected as the classifier.In order to verify the performance of this model, this paper adopts the NASA MDP data set which is widely used in the field of software defect prediction.This paper use the CM1, JM1, PC1 and KC1 dataset to contrast KPCA -SVM model with a single SVM and LLE - SVM. This paper proved that KPCA - SVM model can better solve the problem of data redundancy of defect prediction data set.it can keep the global characteristics, and can have better prediction precision.
Scientific Principles of Space, Time, and Perception

In this paper, the scientific method is brought to task in explaining what is real and can be proven to exist in the real world, time and time again, not what is unreal, not what is stochastic, not what cannot be given any guarantee of measurable reliability in time. Here is defined a common scientific principle for space, time, and perception, to set a complete and consistent foundation for scientific principles of space, time, and perception, based on data that is real; certain scientific principles of space, time, and perception have become obvious through the course of the lead-up papers [1]-[17], and these scientific principles are presented in a consistent and complete manner.
Federated Learning with Mutually Cooperating Devices: A Consensus Approach Towards Server-Less Model Optimization

Federated learning (FL) is emerging as a new paradigm for training a machine learning model in cooperative networks. The model parameters are optimized collectively by large populations of interconnected devices, acting as cooperative learners that exchange local model updates with the server, rather than user data. The FL framework is however centralized, as it relies on the server for fusion of the model updates and as such it is limited by a single point of failure. In this paper we propose a distributed FL approach that performs a decentralized fusion of local model parameters by leveraging mutual cooperation between the devices and local (in-network) data operations via consensus-based methods. Communication with the server can be partially, or fully, replaced by in-network operations, scaling down the traffic load on the server as well as paving the way towards a fully serverless FL approach. This proposal also lays the groundwork for integration of FL methods within future (beyond 5G) wireless networks characterized by distributed and decentralized connectivity. The proposed algorithms are implemented and published as open source. They are also designed and verified by experimental data.
Image super-resolution via multi-view information fusion networks

In recent years, convolution neural networks (CNN) have achieved substantial advantages for single image super resolution (SISR). However, as the depth and width of the networks increase, the model parameters and computation become more complicated. To solve these problems, we propose a lightweight network structure for super-resolution which adopts the multi-view information fusion strategy. The proposed network consists of a feature extraction block, N channel-fusion enhancement blocks (CFEBs) and an upscale block. In order to reduce model parameters and computation complexity, each CFEB adopts group convolution operation and channel-fusion strategy to fuse image information from different channels. In addition, we propose a global-relation attention block to enable interactions among groups and an enhancement block to better extract and fuse multi-scale feature information. Experimental results on public datasets demonstrate that both the proposed model and its lightweight version achieve superior performance than their state-of-the-art couterparts. Moreover, the proposed lightweight model sometimes even has better performance than some state-of-the-art heavy models.
Concerns of cyber criminality in South Africa, Ghana, Ethiopia and Nigeria: rethinking cybercrime policy implementation and institutional accountability

Undoubtedly, some African states have put in place cybercrime legal frameworks and institutional policies to combat cybercrime and curtail the proliferation of the crime globally. Although the cybercrime policy implementation gap is a global problem, this paper notes that these African States have a peculiar challenge of implementation gap or lack of implementation of cybercrime legal frameworks and policies as a basis for cybercrime proliferation and this poses great concerns for internet users. Incentive is comparatively drawn from the United States of America and the United Kingdom in determining what these African governments and institutions should do towards fighting cybercrime.
A Combined method to model policy interventions for local communities based on people knowledge

Policy interventions to promote innovative industries in peripheral regions are often hampered by lack of information on the functioning of the local socio-economic systems, due to their complexity. This might result in mismatches between policy objectives and the actual needs and capability of local communities. To overcome this drawback, it is crucial to obtain appropriate knowledge on the local system, which nevertheless is typically embedded in local actors' minds in uncodified and tacit form. Fuzzy Cognitive Maps (FCMs) have been employed to decode this kind of knowledge in a reproducible manner. However, some problems remain as to how to integrate the necessary vagueness of local actors' heuristic with experts' knowledge into a rational framework. The following methodology customization is proposed: • Combine the FCMs with the Discourse Analysis to obtain relevant narratives (i.e. concepts, visions, insights, etc.) needed to define system boundaries and variables. • Employ individual interviews - rather than a participatory approach - to define the causal relations among system variables. • Integrate tacit and uncodified knowledge embedded in local actors within experts' scientific knowledge.
An Unorthodox Way of Farming Without Intermediaries Through Blockchain

Blockchain technology has the scope of creating a decentralized environment where any third-party organization does not manipulate transactions, and the transaction is recorded in a public ledger permanently within the first-ever occurrence. The backbone of the developing countries' economy depends on farming. When farmers sell their crops, they don't get their desired money following their hard work due to the existence of a loop that always creates an artificial crisis, which causes rise to the price of the farming goods. As a result, the farmer doesn't get this extra money as a middle-man between the farmers and the consumers are taking this extra money out. Though modern farming involves science and technology, still it is centralized and not transparent to all. Based on blockchain technology, the study proposed a decentralized agricultural platform, named KHET to resolve the mentioned issue. This paper constitutes trust and decentralization between agricultural stakeholders such as farmers, supply companies, and markets.
Improved deep embedding learning based on stochastic symmetric triplet loss and local sampling

Designing more powerful feature representations has motivated the development of deep metric learning algorithms over the last few years. The idea is to transform data into a representation space where some prior similarity relationships between examples are preserved, e.g., distances between similar examples being smaller than those between dissimilar examples. While such approaches have produced some impressive results, they often suffer from difficulties in training. In this paper, we introduce an improved triplet-based loss for deep metric learning. Our method aims to minimize distances between similar examples, while maximizing distances between those that are dissimilar under a stochastic selection rule. Additionally, we propose a simple sampling strategy, which focuses on maintaining locally the similarity relationships of examples in their neighborhoods. This technique aims to reduce the local overlap between different classes in different parts of the embedded space. Experimental results on three standard benchmark data sets confirm that our method provides more accurate and faster training than other state-of-the-art methods.
Applying Matrix Factorization In Collaborative Filtering Recommender Systems

Collaborative filtering plays a vital part in advancing the recommendation environment by using the matrix factorization (MF) decomposition technology which is demonstrated to be most successful recommendation strategies. Despite being a successful method used in recommendation systems, SVD-based methods suffer from the data sparsity problem, which leads to inaccurate prediction of ratings. This paper proposes an incorporation-based recommendation method, to address the issue of sparsity in SVD-based strategies. Initially, similar users and items are found. Then, data is generated according to co-rated values. Finally, the data is incorporated into the SVD framework. We implemented our method on MovieLens 100k dataset. Experiment results represent that our method of prediction is better than the existing system.
TangoChain: A Lightweight Distributed Ledger for Internet of Things Devices in Smart Cities

Internet of Things (IoT) is an emerging technology that have been attracting significant attention from diverse application domains ranging from smart homes, smart cities to smart healthcare and manufacturing. Especially in smart city context, IoT devices play a significant role since the city of the future is an interconnected one, where distributed devices communicate with one another, constantly exchanging streams of real time data. However, the fundamental challenge on deploying such technology is the inherent limitations on IoT device's computational power, network connectivity, energy consumption, which in turn hinders secure deployment. The introduction of Blockchain technology for digital cash offers a promising data security scheme for IoT applications in trust-less environments. Blockchain promises an elegant approach for secure information dissemination, machine-to-machine micro-transactions, and access control in a smart city context for IoT devices. However, the computational and storage requirements for original Blockchain model presents difficulties for IoT deployment. In this paper, we propose an experimental lightweight distributed ledger for IoT devices, TangoChain, utilizing a directed acyclic graph as underlying ledger data structure. We discuss the rationale behind our design approach, then present our in-progress proof-ofconcept prototype implementation details, and highlight the potentials for secure IoT device deployments in a smart city context.
Balanced scheduling of distributed workflow tasks based on clustering

Distributed computing, such as Cloud, provides traditional workflow applications with completely new deployment architecture offering high performance and scalability. However, when executing the workflow tasks in a distributed computing environment, significant scheduling overheads are generated. Task clustering is a key technology to optimize process execution. Unreasonable task clustering can lead to the problems of runtime and dependency imbalance, which reduces the degree of parallelism during task execution. In order to solve the problem of runtime imbalance, we propose Runtime Balance Clustering Algorithm (RBCA), which employs the Backtracking approach to make the runtime of each cluster more balanced. In addition, to address the problem of dependency imbalance, we also propose Dependency Balance Clustering Algorithm (DBCA), which defines the dependency correlation to measure the similarity between tasks in terms of data dependencies. The tasks with high dependency correlation are clustered together so as to avoid the dependency imbalance to most extent. We conducted extensive experiments on the WorkflowSim platform and compared our algorithms with the existing task clustering algorithms. The results show that RBCA and DBCA significantly reduce the execution time of the whole workflow.