Reviewerly/exHarmony huggingface dataset:
Risk Simulation Challenges and Success Driven Project Management

To be reliable, project risk simulation must take into account all existing constraints, simulate risk events that may occur and risk responses that management will apply in case they occur, simulate corrective actions that management will take in case project performance gets worse (or better) than expected. The main problems with risk simulation tools include modeling the consequences of uncertainty rather than its source, missing correlations between probability distributions of project parameters, missing corrective actions people apply when risks occur and threaten project goals, using different leveling heuristics than those used for managing projects with resource constraints. A correct project risk model can address the first three of the above problems but the last one is the most serious and cannot be solved if you are using a risk simulation tool which is external to project scheduling software you manage your project with. Probability distributions created by any external risk simulation tool are not reliable when project schedule has resource constraints. Success probability is the probability of meeting project target. Success probability trends are the best performance indicators. Negative trends show that project contingency reserves are consumed faster than expected and corrective actions should be considered.
RCRE: radical-aware causal relationship extraction model oriented the medical field

In the massive medical texts, the accuracy of causal relationship extraction is relatively low because of its special characteristic, a high correlation between semantics and radicals. To improve the extraction accuracy, this paper proposes a radical-aware causal relationship extraction model, which is oriented to the medical field. The BERT pre-training model is used to extract character-level features, which contain rich context information. To further deeply capture the semantics of characters, the Word2Vec model is utilised to extract radical features. Finally, the above two features are concatenated and passed into the extraction model to obtain the extraction results. Experimental results show that the proposed model can improve the accuracy of causal relationship extraction in medical texts.
Federated Recognition Architecture Based on Voting and Feedback Mechanisms for Accuracy Object Classification in Distributed Edge Intelligence Environment

Deep learning has recently become a crucial tool to solve many complex problems and has the potential to revolutionize industries. With the widespread adoption of the Internet of Things, there are now many devices with limited computational resources that are capable of running deep learning models. It has opened up new opportunities to implement deep learning in the edge environment so that decisions can be made locally without sending data to a cloud server for processing. However, because of limited resources, the model’s performance and communication overhead are challenges when deploying learning models to edge devices. The focus of this article is to investigate and propose a federated recognition architecture for object classification in a distributed edge intelligence environment. Specifically, we build an edge server that includes a voting module and a feedback module to improve the overall accuracy of object classification. The voting module aggregates predictions of multiple edge devices, whereas the feedback module sends the voting results to edge devices to adjust the local deep learning model. We build edge devices based on the EdgeX platform which makes it easy to manage data and optimize communication overheads. Because the edge server and edge nodes only exchange prediction results, our proposed architecture ensures security with sensitive data as well as deep learning model architecture. By testing on the image dataset, we evaluate the proposed architecture’s performance and show that it outperforms individual local models in terms of accuracy. Furthermore, our experiments demonstrate that, with the feedback mechanism, the deep learning model is constantly updated with new data to maintain accuracy and avoid being outdated. Besides, we prove the real-time processing speed by collecting the delay time of the proposed model. The results show that our proposed architecture has the potential to be deployed in practical applications such as smart cities and surveillance systems.
Towards an Explainable Artificial Intelligence Approach for Ships Detection from Satellite Imagery

In recent years, ship detection and classification from remote satellite imagery using advanced Artificial Intelligence (AI) algorithms raises great attention in military and civilian applications, such as marine resources monitoring, maritime rescue assistance, protection in territorial sea areas and so on. In this contest, here, the “Ships in Satellite Imagery” Kaggle dataset is taken into account and a customised Convolutional Neural Network (CNN) is proposed to perform the 2-way classification task: presence of ship (denoted as ship class) versus no ship presence (denoted as no-ship class), reporting an accuracy of 98.83%. Furthermore, the explainability of the developed neural network is also investigated by means of an occlusion sensitivity analysis in order to explore which part of the satellite images was involved mostly in the discrimination procedure. The promising outcomes together with the xAI based methodology encourage the use of the proposed ship detection model in space sector.
Architecture for Initial States Algorithm for Blockchain Scalability in Local OnPrem IIoT Environments

Day by day, IIoT applications are more and more used in the industry. In addition, the enormous amount of data generated by these devices must be stored securely and immutably. These criteria find a point of convergence in blockchain technology. An important challenge that companies must face when using IIoT is the need to implement network and telecommunications systems, sometimes without access to cloud applications. These situations demand the development of an architecture and implementation of an algorithm that contributes to the scalability of the blockchain, through the generation of initial states, in local environments. In the present work, the preliminary results of the experiments carried out with the algorithm of initial states are exposed, where the storage space is favored by the compression part of the blockchain.
Introduction

Pervasive, miniaturised and smart sensing devices are deployed in the physical world that work collectively to gather the required data/information, and this mechanism is often referred as ubiquitous sensing, smart dust, and Internet of Things (IoT) (Sandhu et al. in IEEE Internet Things J 8(18):13825–13848, (2021), [1]).
Simultaneous Sensing and Energy Harvesting

In contrast to conventional activity sensors, energy harvesters provide both energy as well as context information. We have discussed in the previous chapter that kinetic, solar, thermal and RF energy harvesters can be used to detect the underlying activity in various applications. Among the available options, KEH is the most common harvesting mechanism, which is used to detect the activity in various applications involving mobility.
Measuring the Validity of a High-stakes English Test in Bangladesh: An Empirical Approach

The SSC English examination is a high-stakes test in Bangladesh because its scores are used to make decisions about the admission, placement, or graduation of students. This study evaluated the empirical validity of the SSC English examination using the correlation coefficient formula by Pearson’s R. The researchers employed a quantitative approach to conducting the research. Data were collected from eight education boards located in Dhaka, Rajshahi, Barisal, Chattogram, Comilla, Dinajpur, Jessore, and Sylhet. The present study used “Simple Random Sampling” while selecting the respondents. The respondents of the study were eighty SSC students. The researcher selected five students from each school. The researcher chose two schools from each education board; one is from a rural area, and another is from an urban area. The respondents were balanced between urban and rural, male and female persons. The findings of the study revealed that the validity of the SSC English test of each education board was subpar. Based on the findings, some recommendations were put forward to improve the SSC English examination in Bangladesh. Notably, the SSC English test items should examine the learners’ cognitive skills, such as thinking, remembering, and reasoning. The question setter should follow the authorized textbook to make a final question to ensure the validity of the test. Moreover, according to the guidelines of the NCTB (2012), candidates’ speaking and listening skills should be measured through continuous assessment. Furthermore, continuous assessment marks could be added to finalize the candidates’ GPA (Grade Point Average) in the summative assessment. The authority ought to train the scorers. The education board should allow the scorers enough time to mark the scripts contentedly to ensure the pragmatism of the test. Moreover, the teachers should teach to the curriculum, not to the test.
Modeling Mental Models: Their Use, Adaptation and Control

Using a proper mental modelMental model during mental processesMental process is often crucial. Such a mental modelMental model has to be learnt and maintained; this involves mental modelMental model adaptationAdaptation. MetacognitionMetacognition is applied to controlControl use and adaptating in a context-sensitiveContext-sensitive manner. In this chapter, a second-order adaptive network modelAdaptive network models for handling mental modelsMental model, covering their use, adaptationAdaptation and controlControl, is discussed and used to illustrate these processes.
DCPV: A Taxonomy for Deep Learning Model in Computer Aided System for Human Age Detection

Deep Learning prediction techniques are widely studied and researched for their implementation in Human Age Prediction (HAP) to prevent, treat and extend life expectancy. So far most of the algorithms are based on facial images, MRI scans, and DNA methylation which is used for training and testing in the domain but rarely practiced. The lack of real-world-age HAP application is caused by several factors: no significant validation and devaluation of the system in the real-world scenario, low performance, and technical complications. This paper presents the Data, Classification technique, Prediction, and View (DCPV) taxonomy which specifies the major components of the system required for the implementation of a deep learning model to predict human age. These components are to be considered and used as validation and evaluation criteria for the introduction of the deep learning HAP model. A taxonomy of the HAP system is a step towards the development of a common baseline that will help the end users and researchers to have a clear view of the constituents of deep learning prediction approaches, providing better scope for future development of similar systems in the health domain. We assess the DCPV taxonomy by considering the performance, accuracy, robustness, and model comparisons. We demonstrate the value of the DCPV taxonomy by exploring state-of-the-art research within the domain of the HAP system.
Heuristic Context-Sensitive Control of Mental Model Aggregation for Multilevel Organisational Learning

Within organisational learningOrganisational learning, aggregationAggregation of developed individual mental modelsIndividual mental model to obtain shared mental modelsShared mental model for the organisationOrganisation is a crucial process. This aggregationAggregation process usually does not only depend on the mental modelsMental model used as input for it, but also on several context factorsContext factor that may vary over circumstances and time. This means that for computational modelingComputational modeling of organisational learningOrganisational learning by adaptive networksAdaptive network, where the formation of a shared mental modelShared mental model is a form of networkNetwork adaptationAdaptation, the underlying aggregationAggregation process better can be controlled by a second-order adaptive dynamical process to obtain a context-sensitiveContext-sensitive way of aggregationAggregation. In this chapter it is explored how context factorsContext factor can be used to model this form of second-order networkNetwork adaptationAdaptation. Indeed, using self-modeling networksSelf-modeling network, mental modelMental model adaptationAdaptation by learningLearning, formation or aggregationAggregation can be modeled in an appropriate manner at a first-order adaptive self-model level, whereas the controlControl over such processes can be modeled at a second-order adaptive self-model level. In this chapter it is shown how by a second-order self-modelSecond-order self-model in such a networkNetwork, contextContext-rich knowledge can be specified that is used to controlControl the aggregationAggregation with the first-order self-modelFirst-order self-model in a context-sensitiveContext-sensitive manner. This is illustrated for heuristicHeuristic knowledge. Based on this controlControl knowledge such an adaptive network modelAdaptive network models can exert context-sensitive controlContext-sensitive control over the mental model aggregationMental model aggregation process. Thus, a computational networkNetwork modeling approach of organisational learningOrganisational learning is presented in which the process of aggregationAggregation of individual mental modelsIndividual mental model to form shared mental modelsShared mental model is controlled in an adaptive context-dependentContext-dependent manner based on heuristicHeuristic knowledge.
Data, Recommendation Techniques, and View (DRV) Model for Online Transaction

With the development of information technology, online transactions, including E-commerce, have been developed. Accordingly, recommendation systems were developed to facilitate customer preferences and increase business revenue. In this paper, our analysis shows that each of these systems was implemented to facilitate the recommendation process of a specific product or service category and applied to a dedicated context. The issue here is if the business provides more than one category of products and/or services it needs to utilize more than one approach to have an effective recommendation process. That would make it more complicated to implement and with a high cost. In addition, each of these systems was developed to overcome a specific problem. There is no guarantee that the system developed to address a dedicated problem could overcome the other problems. Examples of these problems include cold-start, data sparsity, accuracy, and diversity. In this paper, we develop Data, Recommendation Technique, and View (DRV) model. We consider this model to be a foundation for a generic framework to develop recommendation systems that overcome the issues mentioned.
Data Management in Industry–Academia Joint Research: A Perspective of Conflicts and Coordination in Japan

This paper aimed to identify and organize issues that emerge due to interinstitutional differences when handling data in industry–academia collaborations. To achieve this, we interviewed personnel affiliated with companies that were conducting joint research with universities. The interviews revealed three main findings. First, issues related to data management can be classified into three types according to the differences in data creation and handling between collaborators. Second, the medical industry handles data using different methods from those employed by other industries. Third, given the expansion of data distribution opportunities, an increasing number of cases in industry–academia joint research cannot be handled under the conventional contract pattern. It is necessary to establish a new scheme for data management in industry-academia collaboration to cope with these issues.
Features and Problems of Forming Teams of Strategic Projects of Russian Universities

Russian universities are involved in the processes of transformation of higher education, which determines the high importance of developing and implementing transformational strategies. In the strategies, universities lay down priority programs and projects for academic leadership and taking strong competitive positions. To implement these programs and projects, teams are required that are able to accept and implement strategic goals, tasks and challenges of the competitive environment. The formation of such teams for strategic projects occurs spontaneously. There are no effective methodologies and methods for forming university project teams. Our research is aimed at overcoming this problem, the purpose of which is to substantiate the specifics of university project teams, identify problems in the formation of these teams and develop ways to eliminate problems. The study was conducted in 2020–2021 using the case study method. The cases of three universities from different regions of Russia are analyzed. The expert survey method was also used. A total of 75 experts representing the universities selected for the study were interviewed. The results of the study were: a list of specific features of university strategic development project teams; a set of problems of forming strategic project teams, recommendations on ways to overcome these problems, taking into account the specifics of university project teams in the recommendations. The directions of improving the formation of teams of strategic projects of universities have been developed. The novelty lies in the role and competence models of strategic project teams adapted to the conditions of universities.
Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

The increase in positive cases of COVID-19 not only affects the health and lifestyle, but also the economy and the stock market. Tech and digital sector stocks can be predicted to be one of the most profitable. Therefore, the prediction of the stock price is required to be able to forecast the prospects of investment in the future. In this study, the prediction of the stock prices of Multipolar Technologies Ltd. (MLPT) was carried out using the support vector regression (SVR) method with bootstrap aggregation (bagging) technique and particle swarm optimisation (PSO) as SVR optimisation. From the results of the prediction process, it is shown that the application of bagging and PSO techniques in predicting stock prices in the technological sector can reduce the root mean squared error (RMSE) value on the SVR, the RMSE value from 22.142 to 21.833. Although it does not have a big impact, it is better to apply a combination of bagging and PSO techniques to SVR than one of them (SVR/SVR - PSO/SVR-Bagging).
A Classification Algorithm Utilizing the Lempel-Ziv Complexity Score for Missing Data

Informative data analysis relies heavily on the quality of the underlying data. Unfortunately, often in our research, the data to be analyzed contains many missing values. While we have methods to mitigate the missing data – listwise deletion, multiple imputation, etc. - these methods are only appropriate for use when data are missing at random. When data are missing not at random, use of these methods leads to erroneous analyses. Determining whether a data set contains random or non-random missing data is an open challenge in our field. An algorithm to categorize missing data utilizing the Lempel-Ziv (LZ) complexity score is proposed by the authors and initial results from its use in both generated and publicly available data are analyzed. The authors’ algorithm contains many positive features. It is useful with data sets of all compositions (string, numerical, graphics, mixed), yields easily interpreted results, and can be used autonomously to determine the type of missingness (random versus non-random). The authors review related literature, explain the algorithm, and interpret initial results of its use with data from canonical Bayesian networks, United States census data, and data sets from the University of California, Irvine machine learning repository. Further usages in the field of bioinformatics and pathways for future research are discussed.
Hybrid grasshopper and ant lion algorithms to improve imperceptibility, robustness, and convergence rate for the video steganography

The need for securing multimedia content from being intercepted is a prominent research issue. This work proposes an optimised video steganography model that improves imperceptibility and robustness by extracting keyframes and calculating the optimal scaling factor. The squirrel search algorithm (SSA) is used to extract keyframes since it ensures distinct position updation processes through Levy flying and predator features, whilst the grasshopper optimisation and ant lion optimisation algorithms are hybridised to compute the optimal value of the scaling factor. In terms of imperceptibility and robustness, the simulation results suggest that the proposed approach outperforms existing data-hiding models. It also discovers the optimal scaling factor in under ten iterations, indicating that the fastest convergence rate is possible.
A Survey on 3D Style Transfer

Image style transfer is a popular and widely studied task in computer vision, and it aims to apply the style of the source image to the target while the target remains its original content. Style transfer is widely used in creating new images in 2D, but style transfer in 3D images still has many challenges. In this paper, we summarize the major existing methods of 3D style transfer, including traditional and neural network based approaches. Moreover, we discuss the application field and the future research direction in 3D style transfer.
On the Hardness of the Minimum Distance Problem of Quantum Codes

We study the hardness of the problem of finding the distance of quantum error-correcting codes. The analogous problem for classical codes is known to be NP-hard, even in approximate form. For quantum codes, various problems related to decoding are known to be NP-hard, but the hardness of the distance problem has not been studied before. In this work, we show that finding the minimum distance of stabilizer quantum codes exactly or approximately is NP-hard. This result is obtained by reducing the classical minimum distance problem to the quantum problem, using the CWS framework for quantum codes, which constructs a quantum code using a classical code and a graph. A main technical tool used for our result is a lower bound on the so-called graph state distance of 4-cycle free graphs. In particular, we show that for a 4-cycle free graph <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> , its graph state distance is either <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta +1$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is the minimum vertex degree of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> . Due to a well-known reduction from stabilizer codes to CSS codes, our results also imply that finding the minimum distance of CSS codes is also NP-hard.