Reviewerly/exHarmony huggingface dataset:
Neural Network Architecture Selection Using Particle Swarm Optimization Technique

Finding the best structure of ANN to minimize errors, the processing and the search time is one of the main objectives in the AI field. In order to achieve prediction with a high degree of accuracy in a short time, an enhanced PSO-based selection technique to determine the optimal configuration for the artificial neural network has been proposed in this paper. To design the neural network to minimize processing time, search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSO with 2-D search space has been employed to select the best hyperparameters in order to construct the best neural network where PSO is used as a decision-making model and ANN is used as a learning model. The suggested technique was used to select the optimal number of the hidden layer and the number of units per hidden layer. The proposed technique was evaluated using a chemical dataset. The result of testing the proposed technique displayed high prediction accuracy with MSE equal to 3.9% and the relative error between the expected output and actual target is less than 1.6%. The results of the comparison of the proposed technique with the ANN showed thatthe proposed approach could predict output with an infinitesimal error, outperforming the existing ANN model in terms of error ratio.
Human Factors in Biocybersecurity Wargames

Within the field of biocybersecurity, it is important to understand what vulnerabilities may be uncovered in the processing of biologics as well as how they can be safeguarded as they intersect with cyber and cyber-physical systems, as noted by the Peccoud Lab, to ensure not only product and brand integrity, but protect those served. Recent findings have revealed that biological systems can be used to compromise computer systems and vice versa. While regular and sophisticated attacks are still years away, time is of the essence to better understand ways to deepen critique and grasp intersectional vulnerabilities within bioprocessing as processes involved become increasingly digitally accessible. Wargames have been shown to be successful within improving group dynamics in response to anticipated cyber threats, and they can be used towards addressing possible threats within biocybersecurity. Within this paper, we discuss the growing prominence of biocybersecurity, the importance of biocybersecurity to bioprocessing, with respect to domestic and international contexts, and reasons for emphasizing the biological component in the face of explosive growth in biotechnology and thus separating the terms biocybersecurity and cyberbiosecurity. Additionally, a discussion and manual are provided for a simulation towards organizational learning to sense and shore up vulnerabilities that may emerge within an organization's bioprocessing pipeline. This article includes an introduction to Biocybersecurity (BCS), highlights games in the methodology used, retrains focus on the biological component, and includes exact instructions for doing a war game on one’s own.
A query expansion method based on topic modeling and DBpedia features

Query Expansion (QE) is a method used for improving Information Retrieval (IR) by adding the terms that are almost selected from feedback documents, and similar to the user query terms. But, due to the very small average number of query keywords; it is sometimes difficult to detect the context around the user query, and expand the query accordingly, especially when it contains ambiguous terms(i.e. polysemy terms). To this end, Linked Open Data (LOD) sources may be exploited. Yet, most attributes from linked data are multi-valued which makes a system unable to determine the right one(s) to use for expansion. And few other attributes are single-valued but too long and noisy to use directly. To deal with the previous issues, integration of the topic modeling process has been proposed to predict the latent semantic attribute-topics to use for expansion. This approach reconstructs candidate documents for a given query using distribution technique Bose-Einstein statistics (Bo1) and DBpedia attributes. The Latent Dirichlet Allocation(LDA) based topic models are then generated by considering these documents and the relevant expansion terms are then determined. The proposed method has been evaluated using the AP dataset collection, and the experiments revealed significant improvements according to the retrieval results using the distribution technique Bo1. Also, the proposed “LDA-LinkedBo1” approach outperformed DBpedia association based approaches in terms of [email protected]
Improving artificial bee colony algorithm using modified nearest neighbor sequence

Nearest neighbor (NN) is a simple machine learning algorithm, which is often used in classification problems. In this paper, a concept of modified nearest neighbor (MNN) is proposed to strengthen the optimization capability of artificial bee colony (ABC) algorithm. The new approach is called ABC based on modified nearest neighbor sequence (NNSABC). Firstly, MNN is used to construct solution sequences. Unlike the original roulette selection, NNSABC randomly chooses a solution from the corresponding nearest neighbor sequence to generate offspring. Then, two novel search strategies based on the modified nearest neighbor sequence are employed to build a strategy pool. In the optimization process, different search strategies are dynamically chosen from the strategy pool according to the current search status. In order to study the optimization capability of NNSABC, two benchmark sets including 22 classical problems and 28 CEC 2013 complex problems are tested. Experimental results show NNSABC obtains competitive performance when compared with twenty-three other ABCs and evolutionary algorithms.
Immersive audio-visual scene reproduction using semantic scene reconstruction from 360 cameras

Abstract As personalised immersive display systems have been intensely explored in virtual reality (VR), plausible 3D audio corresponding to the visual content is required to provide more realistic experiences to users. It is well known that spatial audio synchronised with visual information improves a sense of immersion but limited research progress has been achieved in immersive audio-visual content production and reproduction. In this paper, we propose an end-to-end pipeline to simultaneously reconstruct 3D geometry and acoustic properties of the environment from a pair of omnidirectional panoramic images. A semantic scene reconstruction and completion method using a deep convolutional neural network is proposed to estimate the complete semantic scene geometry in order to adapt spatial audio reproduction to the scene. Experiments provide objective and subjective evaluations of the proposed pipeline for plausible audio-visual VR reproduction of real scenes.
Corporate social responsibility and traditional practices recognised as violence against women in Nigeria’s oil region

We examine the impact of multinational oil companies’ (MOCs’) corporate social responsibility (CSR) on traditional practices recognised as violence against women and girls (VAWG) in Nigeria’s oil region. Results from a combined propensity score matching and logit model indicate that MOCs’ CSR plays a significant role in empowering women and girls with information and education to protect their human rights. It is implied that CSR offers an opportunity for MOCs to help address the prevalence of child early and forced marriage, female genital mutilation/cutting, sex trafficking, virginity testing, and taboos through a business case for stakeholders’ human rights protection.
Constrained Trajectory Optimization With Flexible Final Time for Autonomous Vehicles

Differential dynamic programming (DDP) is a well-recognized method for trajectory optimization due to its fast convergence characteristics. However, the original DDP requires a predefined final time and cannot handle nonlinear constraints in optimization. This prohibits the application of DDP for autonomous vehicles due to the heuristic nature of setting a final time beforehand and the existence of inherent physical limits. This article revisits DDP by dynamically optimizing the final time via the first-order optimality condition of the value function and using augmented Lagrangian method to tackle the issue of nonlinear constraints. The resultant algorithm is termed as flexible final time constrained differential dynamic programming (FFT-CDDP). Extensive numerical simulations for a three-dimensional guidance problem are used to demonstrate the working of FFT-CDDP. The results indicate that the proposed FFT-CDDP provides much higher computational efficiency and stronger robustness against the initial solution guess, compared with the off-the-shelf general purpose optimal control software (GPOPS) algorithm.
A hierarchical planning framework of the intersection with blind zone and uncertainty

It is important for autonomous vehicles to plan at the unsignalized intersection due to the uncertainty of visible vehicles and phantom vehicles in the blind zone. This paper proposes a hierarchical framework to optimize the left-turn planning at the intersection to solve this problem. The framework is composed of a higher-level candidate paths selector and a lower-level Partially Observable Markov Decision Process planner. All possible vehicle distributions in the blind zone are represented in the form of a set, which makes the method have the good universality. The simulation experiments show that autonomous vehicle can pass the intersection safely and efficiently.
Modeling of a Generic Edge Computing Application Design

Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.
Blind Image Super Resolution Using Deep Unsupervised Learning

The goal of single image super resolution (SISR) is to recover a high-resolution (HR) image from a low-resolution (LR) image. Deep learning based methods have recently made a remarkable performance gain in terms of both the effectiveness and efficiency for SISR. Most existing methods have to be trained based on large-scale synthetic paired data in a fully supervised manner. With the available HR natural images, the corresponding LR images are usually synthesized with a simple fixed degradation operation, such as bicubic down-sampling. Then, the learned deep models with these training data usually face difficulty to be generalized to real scenarios with unknown and complicated degradation operations. This study exploits a novel blind image super-resolution framework using a deep unsupervised learning network. The proposed method can simultaneously predict the underlying HR image and its specific degradation operation from the observed LR image only without any prior knowledge. The experimental results on three benchmark datasets validate that our proposed method achieves a promising performance under the unknown degradation models.
A two-phase half-async method for heterogeneity-aware federated learning

Federated learning (FL) is a distributed machine learning paradigm that allows training models on decentralized data over large-scale edge/mobile devices without collecting raw data. However, existing methods are still far from efficient and stable under extreme statistical and environmental heterogeneity. In this work, we propose FedHA ( Fed erated H eterogeineity A wareness), a novel half-async algorithm which simultaneously incorporates the merits of asynchronous and synchronous methods. It separates the training into two phases by estimating the consistency of optimization directions of collected local models. It applies different strategies to facilitate fast and stable training, namely model selection, adaptive local epoch, and heterogeneity weighted aggregation in these phases. We provide theoretical convergence and communication guarantees on both convex and non-convex problems without introducing extra assumptions. In the first phase (the consistent phase), the convergence rate of FedHA is O 1 e T , which is faster than existing methods while reducing communication. In the second phase (inconsistent phase), FedHA retains the best-known results in convergence ( O 1 T ) and communication ( O 1 ∊ ). We validate our proposed algorithm on different tasks with both IID (Independently and Identically Distributed) and non-IID data, and results show that our algorithm is efficient, stable, and flexible under the twofold heterogeneity using the proposed strategies.
An optimization measurement method of laser sensor based on perspective projection model

Perspective projection model is the common measurement method for laser sensor to calculate the 3D coordinates of spatial points. However, the error of mathematical model is introduced, which inevitably decrease the measurement accuracy of laser sensor. In order to improve the accuracy, an optimization measurement method is proposed in this paper. Firstly, the structural design and measurement principle of laser sensor is developed. Secondly, the error propagation model is established, and the effect of the structural parameters in perspective projection model is probed in detail. Next, an optimization measurement method is presented by adjusting the structural parameters, which is the projection angle of laser beam in measurement coordinate system. Finally, the effectiveness of the proposed optimization method is evaluated by the simulative and practical experiments. The experiment results show that the maximum error can be reduced to 0.589 mm with optimization from 1.090 mm of previous method. Enough theoretical research is offered for optimization design of measurement model of the laser sensor.
Sentiment Analysis on Movie Review using Naïve Bayes

Sentiment analysis is needed because the analysis of the emotion is somehow complicated due to slang phrases, misspellings, short-forms, recurring characters, the use of dialects, and modern emoticons. The aim of this project is to develop a system to analyze the sentiment of movie reviews and visualize the result of sentiment analysis. The dataset was captured from IMDb movie reviews. This dataset contains 50,000 instances with two columns which are the reviews and their sentiments. The data set undergoes a cleaning process to extract meaningful data. Two types of features have been investigated, namely, the Bag of Words (BoW) and the TF-IDF modelling with Naïve Bayes classifiers. The result of accuracy is 89% which indicates that the Naïve Bayes can be used for sentiment analysis in any movie review task.
Adversarial Reprogramming of Pretrained Neural Networks for Fraud Detection

Machine learning models have been widely used for fraud detection, while developing and maintaining these models often suffers from significant limitations in terms of training data scarcity and constrained resources. To address these issues, in this paper, we leverage machine learning vulnerability to adversarial attacks, and design a novel model AdvRFD that Adversarially Reprograms an ImageNet classification neural network for Fraud Detection task. AdvRFD first embeds transaction features into a host image to construct new ImageNet data, and then learns a universal perturbation to be added to all inputs, such that the outputs of the pretrained model can be accordingly mapped to the final detection decisions for all transactions. Extensive experiments on two transaction datasets made over Ethereum and credit cards have demonstrated that AdvRFD is effective to detect fraud using limited data and resources.
Motion Sickness Conditioning to Reduce Cybersickness

We present a remote longitudinal experiment to assess the effectiveness of a common motion sickness conditioning technique (MSCT), the Puma method, on cybersickness in VR. Our goal was to evaluate benefits of conditioning techniques as an alternative to visual cybersickness reduction methods (e.g., viewpoint restriction) or habituation approaches which ”train” the user to become acclimatized to cybersickness. We compared three techniques - habituation, the Puma method conditioning exercise, and a placebo (Tai Chi) - in a cybersickness-inducing navigation task over 10 sessions. Preliminary results indicate promising effects.
Artificial Intelligence based Risk Management Framework for Distributed Agile Software Development

Distributed Agile Software Development blends the principles of agile to geographically separated teams and thereby combines the quality benefits of Agile Software Development with cost benefits of Distributed Software Development. Distributed Agile Software Development brings along new risks that arise due to the difference in working principles of Agile Software Development and Distributed Software Development. Identifying and managing these risks on time improves the quality of the software product and reduces the overall development cost. This work presents a novel Artificial Intelligence based framework for managing risks in Distributed Agile Software Development. The proposed framework will help the practitioners to timely identify and deal with associated risks and thereby reduce the cost and development time.
Interpretation of geochemical anomalies and domains using Gaussian mixture models

The element's concentrations in stream sediments are frequently the product of complex geological processes and a variety of lithological sources. This variety of processes dilutions or concentrates the trace elements in the sediments concerning the original concentration presented in the source rock. When analyzing the geochemical data of stream sediments, it is common to find skewed distributions. This skewness can be attributed to multiplicative effects involved in dilution processes that result in lognormal distributions. It also can imply a mixture of populations that results from the variety of geological processes and/or the diversity of lithological sources. In this study, a method is proposed to classify geochemical samples of sediments into data sets or geochemical domains that follow normal, lognormal or log-ratio normal distributions. This methodology can be based on the classification of samples according to geological features or use Gaussian mixture models to determine the geochemical domains. To mixture modeling, CLR transformation and the expectation-maximization (EM) algorithm were used. The results obtained include mixture models of 2–3 populations associated with main geochemical processes and/or dominant lithological sources. The proposed methodology defines threshold values for each domain and allows to set a more appropriate background. An analysis carried out with data from northern Mexico identified anomalies that would not be defined without the separation of previous domains.
Sun-Glare region recognition using Visual explanations for Traffic light detection

In order to achieve automated driving on public roads, image-based recognitions such as object detection and traffic light detection are significant technologies to understand surrounding road situations. However, the automated vehicle might be faced severe situations for sensing due to heavy sunshine. If the image captured by the onboard camera is overexposed, the image information will be lost and there is a risk of false detection. In particular, in traffic light detection where the acquisition of color information is essential, if the front traffic light and the sun overlap during intersection driving, it will not be possible to make an appropriate intersection approach judgment. Therefore, this paper developed the method to recognize sun-glare regions in the image using visual explanations of Convolutional Neural Network (CNN). The CNN outputs an attention map using the Grad-CAM method, and then the global direction of the sun-glare region can be estimated by time-series processing. The developed method contributes to implementing robust image recognitions by estimating the direction in which visibility is reduced by sunlight such as direct sunlight and reflected light from buildings.
Dependency Parser for Hindi Using Integer Linear Programming

This paper is an attempt to show a dependency parser for Hindi Language using Integer Linear Programming. A new approach is described for developing the constraint parser by convexifying the integer values taken as input, depicting the source groups (nouns) and demand groups (verbs) in a sentence. The convexified input is further used to find the Integer dependency values, which validates the word’s dependency relations in any given Hindi Language sentence. Based on the Karaka constraint conditions, Karaka charts are computed by referencing the Karaka word values obtained from the constraint formulations, which solve the inequalities generated in a sentence from its source groups and demand groups based on the concepts of Paninian Grammar.
Machine Learning in Requirements Engineering: A Mapping Study

Machine learning (ML) techniques are used to make the software development process more efficient and effective. Many ML approaches have also been proposed to automate Requirements Engineering (RE) activities such as ambiguity detection, traceability analysis and to address complex RE challenges. The overall goal of this research is to explore the state of the art of application of ML in RE and to determine the effectiveness of ML in improving the RE process and artefacts. Following the Evidence-Based Software Engineering approach, we performed a mapping study of the empirical studies on ML techniques and approaches used in RE published between 2010 and April 2020. Data were extracted from the selected papers about the ML techniques, problems, and challenges of using ML, identification of the used datasets, and the evaluation metrics employed to assess the ML techniques. We analyzed 65 relevant papers in this mapping study. Our analysis shows that ML is an effective tool for automating RE analysis tasks, overcoming complexity, and reducing cost and time. We also present the gaps in the ML for RE literature and suggest areas that need further research.