Miracle-dz/UniFAR-FTUs huggingface dataset:
Systems Thinking in Software Projects-an Artificial Neural Network Approach

The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create project-outcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success.
[Invited] Ultra-High-Capacity Weakly Coupled Mode-Division-Multiplexed Transmission

Mode division multiplexing (MDM) is attractive for increasing the transmission capacity. This paper reviewed weakly-coupled MDM techniques as approaches for reducing MIMO complexity. In addition, we present demonstration results of 402.7-Tb/s weakly-coupled 10-mode-multiplexed transmission.
Real-time Energy Management of Microgrid Using Reinforcement Learning

Driven by the development and application of smart grid and renewable energy sources (RES) generation technologies, microgrid (MG) plays an important role in environmental protection and optimization of the grid structure by integrating local loads and distributed energy. However, the stochastic and intermittent nature of RES have caused difficulties in the economic energy dispatching of MG. Inspired by reinforcement learning (RL) algorithms, this paper proposes a novel learning-based control MG scheduling strategy. Unlike traditional model-based methods that require predictors to estimate stochastic variables with uncertainties, the proposed solution does not require an explicit model. The proposed method is simulated in the environment composed of realistic data, and the effectiveness of the method is explained and verified.
Computer Simulation of the Bias Field in the Antiferromagnetic-Ferromagnetic Bilayer System

In this article, computer modeling of the two-layer system is performed. There are two films in the system. The first film is layered antiferromagnetic. The second film is ferromagnetic. The Ising's model is used. A combined approach from the Wolf algorithm and the Metropolis algorithm is used to model the behavior of the system in the magnetic field. Hysteresis loop for various system parameters is obtained. The amount of the bias field is calculated based on the hysteresis loop offset. The bias field is linearly dependent on the exchange interaction at the film boundary.
Economic Load Dispatch with Short-Term Wind Power: Machine Learning through Radial Basis Neural Network

In economic load dispatch (ELD) complemented by wind power, it is highly desirable to develop a quick scheme to adjust the real power generated by thermal units. In this paper, we apply the machine learning (ML) methodology to solve such a problem. Two power systems are chosen as case studies. The first system consists of 15 generators and 100 wind turbines, while the second system consists of 40 generators and 250 wind turbines. The ML scheme is implemented with the radial basis neural network (RBN). All stages of ML, namely training and testing, are covered in the simulation. It is shown that, by using the trained RBN, the time of solving ELD is significantly reduced.
Lip Reading Sentences Using Deep Learning With Only Visual Cues

In this paper, a neural network-based lip reading system is proposed. The system is lexicon-free and uses purely visual cues. With only a limited number of visemes as classes to recognise, the system is designed to lip read sentences covering a wide range of vocabulary and to recognise words that may not be included in system training. The system has been testified on the challenging BBC Lip Reading Sentences 2 (LRS2) benchmark dataset. Compared with the state-of-the-art works in lip reading sentences, the system has achieved a significantly improved performance with 15% lower word error rate. In addition, experiments with videos of varying illumination have shown that the proposed model has a good robustness to varying levels of lighting. The main contributions of this paper are: 1) The classification of visemes in continuous speech using a specially designed transformer with a unique topology; 2) The use of visemes as a classification schema for lip reading sentences; and 3) The conversion of visemes to words using perplexity analysis. All the contributions serve to enhance the accuracy of lip reading sentences. The paper also provides an essential survey of the research area.
Information Technologies in Optimization of Panels Stretch Forming Processes

The modeling of the double curvature shell shaping technology using CAE-systems is considered. To ensure high accuracy and quality of parts, it is necessary to perform a complete analysis of full-size theoretical models. When setting up an automated shaping of parts with a stretch forming, it is necessary to develop a control program and an electronic model of a punch. On condition the specified punch, the problem of optimal control is posed to find the best trajectory of clamps movement in the equipment. The value of material damage in plasticity is considered as an optimization criterion. A discrete optimal control problem is formulated, which is solved by the dynamic programming method. The space of solutions to optimal control problems must include non-monotonic deformation trajectories that take into account partial unloading. The article presents the implementation of a numerical method for optimizing the deformation trajectory of a panel on a stretch-forming punch in the finite element system MSC.Marc.
Biological and chemical degradation of ionic ethyllead compounds in soil

Degradation rates and formation of organolead metabolites of ionic triethyllead (TREL) and diethyllead (DEL) in nonsterile and autoclaved surface and subsurface soil samples of Arredondo fine sand were determined using {sup 14}C-labeled chemicals. Both [{sup 14}C]TREL and [{sup 14}C]DEL in nonsterile and autoclaved soils initially declined rapidly, and both chemicals initially disappeared more rapidly from the surface sample treated with [{sup 14}C]DEL. In this soil, [{sup 14}C]DEL in the autoclaved sample disappeared more rapidly than that in the nonsterile sample. The [{sup 14}C]DEL was briefly detected in [{sup 14}C]TREL-treated soil samples, and [{sup 14}C]DEL remained in the autoclaved samples longer than in the nonsterile samples. Both [{sup 14}C]TREL and [{sup 14}C]DEL in non-sterile surface and subsurface samples were initially mineralized rapidly, and at the end of 31 and 28 d of incubation, 15 to 16% and 18 to 19% of the applied {sup 14}C was mineralized, respectively. Mineralization was not observed in autoclaved soil samples. It was concluded that both biological and chemical degradation of TREL and DEL in soil occurred, and chemical degradation was probably the major factor contributing to the disappearance of TREL and DEL in soil. The exact extent of chemical degradation is not known. Chemical andmore » physical properties of soil could be altered significantly by autoclaving, and may account for the increase in chemical degradation rates. « less
Video Processing Method for High-Definition Maps Generation

This paper proposes a three-phase method for generating high-definition (HD) maps based on the analysis of video recordings obtained from DVRs or smartphones mounted in a vehicle. The method uses a convolutional neural network (CNN) as a classifier of map object types on the video frames. The calculation of the distances while HD-map generating is carried out using a mathematical model taking into account three-dimensional space and tilt angles of the camera. CNN had trained on the KITTI image set to determine the asphalt surface, then it applied to a video record with 1800 frames to generate a layer of HD-map. The test results showed a measurement error is about 5% at a distance of 15 meters from the camera, thus, the proposed method could be used as a tool for HD-maps generation.
Predicting the level of generalized anxiety disorder of the coronavirus pandemic among college age students using artificial intelligence technology

Introduction: Emerging reports indicate heightened anxiety among university students during the Corona pandemic. Implications of which can impact their academic performance. Artificial intelligence (AI) through machine learning can be used to predict which students are more susceptible to anxiety which can inform closer monitoring and early intervention. To date, there are no studies that have explored the efficacy of AI to predict anxiety among college students. Objective: to develop the best fit model to predict anxiety and to rank the most important factors affecting anxiety. Method: Data was collected using an online survey that included general information; Covid-19 stressors and (GAD-7). This scale categorizes level of anxiety to none, mild, moderate, and severe. We received 917 survey answers. Several machine learning classifiers were used to develop the best fit model to predict student level of anxiety. Results: the best performance based on AUC is AdaBoost (0.943) followed by neural network (0.936). Highest accuracy and F1 were for neural network (0.754) and (0.749) respectively, then neural network selected to be the best fit model. The three scoring methods revealed that the top three features that predicted anxiety to be gender; sufficient support from family and friends; and fixed family income. Conclusion: Neural network model can assist college counselors to predict which students are going through anxiety and revealed the top three features for heightened student anxiety to be gender, a support system, and family fixed income. This information can alter college councilors for early mental intervention.
Spotting misinformation to limit the impact of disruption on society by using machine learning

Deceptive information attracts most and it creates most dangerous impact on society. As we know, fighting against pandemic is as dangerous as fighting against infodemic, so we have to find a solution to limit the impact of disruption on society. To win the battle, first we need to spot misinformation and in this case machine learning gives us a stunning result. To put a stop to the spread of viral deceptive information, it is important to identify them first. In this paper, after introducing the dataset, various operations are done, where natural language processing (NLP) plays an important role. Here, machine learning algorithm recurrent neural network (RNN), convolutional neural network (CNN), support vector machine (SVM), naïve Bayes are used to spot misinformation. In this paper, the future research direction, the challenges are also mentioned. To overcome such problems the predicted solution is also discussed.
A Superficial Analysis Approach for Identifying Malicious Domain Names Generated by DGA Malware

Some of the most serious security threats facing computer networks involve malware. To prevent malware-related damage, administrators must swiftly identify and remove the infected machines that may reside in their networks. However, many malware families have domain generation algorithms (DGAs) to avoid detection. A DGA is a technique in which the domain name is changed frequently to hide the callback communication from the infected machine to the command-and-control server. In this article, we propose an approach for estimating the randomness of domain names by superficially analyzing their character strings. This approach is based on the following observations: human-generated benign domain names tend to reflect the intent of their domain registrants, such as an organization, product, or content. In contrast, dynamically generated malicious domain names consist of meaningless character strings because conflicts with already registered domain names must be avoided; hence, there are discernible differences in the strings of dynamically generated and human-generated domain names. Notably, our approach does not require any prior knowledge about DGAs. Our evaluation indicates that the proposed approach is capable of achieving recall and precision as high as 0.9960 and 0.9029, respectively, when used with labeled datasets. Additionally, this approach has proven to be highly effective for datasets collected via a campus network. Thus, these results suggest that malware-infected machines can be swiftly identified and removed from networks using DNS queries for detected malicious domains as triggers.
Network Architecture Optimization for Area Surveillance in Multi-static Radar Systems

This paper describes a network architecture optimization method for area surveillance in multistatic radar systems. The proposed method addresses the problem of contradiction between the network performance, and the limited communication and computing ability of the multistatic radar systems. The goal is to improve the system surveillance performance by optimizing the network architecture of such systems. We take the coverage ratio of the surveillance area as the optimization index, consider the communication and calculation abilities of radar nodes as constraints, and establish an optimization model for the network architecture. Two methods of data processing for detection are considered to enhance the node communication and computation abilities. It is shown that the optimization problem is nonconvex, and a clustering-based sequential optimization algorithm is proposed as a sub-optimal solution method. Finally, simulation results verify the validity and effectiveness of the proposed method.
Reinforcement Learning Lifecycle for the Design of Advanced Robotic Systems

Machine learning is a recognised technology for problem solving and accelerates the automation by enabling systems to act independently. Cyber-physical systems based on machine learning enable factories to increase the skills of robotic systems. The lack of standardised tools and workflows for artificial intelligence (AI) rises the importance of research methodologies and frameworks for industrial application. First concepts have shown potential for a combination of holistic development methodologies and AI. We present a Reinforcement Learning Lifecycle (RLL) for the development of advanced robots. The autonomous software agent can furthermore lead to the automated optimisation of the systems. The virtual-based method speeds up learning processes, improve development and operation processes by the evaluation of multiple simulation environments. We show how an AI-based methodology assists the development of advanced robots along the product lifecycle. The first implementations show potential regarding the usability and results of the approach during different development processes.
Political Fake Statement Detection via Multistage Feature-assisted Neural Modeling

Fake news detection has recently gained much attention from the wider NLP community due to its importance for preventing the spread of misinformation and its negative impact through the social media. The goal of this task is to classify the veracity labels of a statement expressed by a politician into fine-grained classes (degrees of truth). Previous deep learning approaches have significantly improved the performance of Political Fake Statement Detection by modeling statement with the speaker’s credit history. However, the credit history may not be available in reality and most approaches did not consider about the evidence that supporting or denying claims when detecting fake news. In addition, state-of-the-art models may struggle to detect fine-grained labels because the statement of the speaker expresses factual and incorrect instances at the same time. In this paper, we approach the Political Fake Statement Detection problem by proposing two multi-stage feature-assisted neural models that consider claims and justifications as an input in a stance detection manner. We explore five-stage and three-stage classification strategies to better discern between the fine-grained labels of fake news. The proposed model in each stage is built on the powerful combination between dual GRU layers and lexical features which we further optimise by using Gaussian Noise. An extensive experimental work on a real-world benchmark LIARPLUS (an extended version of LIAR) dataset shows that three-stage model achieves state-of-the-art Accuracy (46.13%) and F1score (45.13%) without using metadata and the credit history of the speaker. We also experimentally show that modeling the credit history in conjunction with statement and justification gives more than 6% improvement (e.g. 52.23% and 52.26% respectively).
A Conceptual Paper on The Influence of Social-Economic Status on Attitude Towards Online Health Information Seeking Behavior: A Malaysian Context

This conceptual paper argues that perceived usefulness, perceived ease of use, and attitude are insufficient in explaining online health information seeking patterns among women. Building on the technology acceptance model, this paper presents socio-economic status/income as a direct influence on the attitude that web users have towards searching for health-related information on the internet. This newly added socio-demographic construct offers an understanding of how social factors can determine attitude towards online health information-seeking behavior. The paper conceptualizes socioeconomic status as another factor that has a direct influence attitude, thus influencing women's decision to use or not to use online health-related content. This study highlights the implications of the positive attitude of women towards online health-related content which sometimes does not translate into the actual use of online health-related information. Thus, by introducing income as a limiting or motivating factor, it helps to explain how the affordability of access to the internet might explain why women have a positive attitude towards the internet through their perceptions of its usefulness and ease of use and yet decide not to utilize it.
Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

In this work, we propose and demonstrate a received-signal-strength (RSS) based visible-light-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions.
Characterization of the main physico-chemical parameters in three styles of craft beer

The beer is a widely consumed drink, appreciated for its sensory characteristics. Beer also contains beneficial compounds for health derived from its raw materials. In recent years there is an expansion of craft breweries in many countries, with a growing market share. Consumers are more and more attracted by craft beers that are perceived as higher quality and with a more complex flavour profile than commercial ones. Drawbacks are the difficulty to standardize method used for their production causing variability in some quality parameters. In this study the results of the main physico-chemical parameters used for the characterization of beer are reported with the aim to highlight as the type of recipe affect the quality of three styles of craft beer: Session India Pale Ale, Dark Ale and Blonde Ale.
Consensus Tracking via Iterative Learning for Multi-Agent Systems With Random Initial States

In this paper, a distributed consensus iterative learning control algorithm is proposed for the finite-time consensus tracking of multi-agent systems with random initial states. The tracking errors from the agent itself and its neighbours are applied to successively rectify the control protocol when only some agents can obtain the desired trajectory information. Additionally, a time interval is designed in the control protocol, and the random initial state errors are rectified, one-by-one, in the time interval. The interval can gradually shorten as the number of iterations increases. Furthermore, the convergence of the algorithm is theoretically proven by the contraction mapping method, and the convergence condition is derived. The proposed algorithm can cause the time interval to gradually shorten with the iterations increase, during the time, the desired trajectory cannot be consistently tracked due to the random initial state errors. As a consequence, the algorithm gradually widens the time interval for full consensus tracking of the multi agents. Finally, the simulation examples are provided to verify the effectiveness of the algorithm.
Feature Importance Ranking for Increasing Performance of Intrusion Detection System

The performance of the Intrusion Detection System (IDS) depends on the quality of the model generated in the training process. An appropriate process positively affects not only the performance but also computational time for detecting intrusions. Reliable training data can be obtained by preprocessing the dataset, which can be feature extraction, reduction, and transformation. Generally, feature selection has become the main problem. In this research, we work on that issue by developing a new method based on Feature Importance Ranking Classification. We propose to reduce the size of the dimension by combining Feature Importance Ranking to calculate the importance of each feature and Recursive Features Elimination (RFE). The results of the experiment show that the proposed method raises the performance over the existing methods. It can be proven by evaluating some metrics: accuracy, sensitivity, specificity, and false alarm rate.
Using Neural Networks to Identify Features Associated with HIV Nef Protein and Cancer

HIV Nef protein has a known association with cancer, yet the specific features associated with this relationship remain poorly characterized. Using a dataset of well curated Nef sequences, we trained evolved neural networks to classify sequences as having originated from cancer or non-cancer samples. This process allows us to identify features that are possibly associated with Nef protein in HIV and its relation to cancer. We review the methods and features identified for their biological significance.