Reviewerly/exHarmony huggingface dataset:
Implementation of Random Forest Regression for COCOMO II Effort Estimation

One of Project Manager early activity is to estimate time, and cost based on given scope, which can help project manager to plan schedule and used resources. Estimation is very important in project management because a bad result of estimation will result in bad management of project and may cause failure. There are methods that can be used to estimate software development effort; COCOMO II is one method that commonly used. Many researcher before have been used algorithm, such as Bat, Bee Colony, or MOPSO to increase COCOMO II estimation accuracy. However, as the technology advanced, there are a lot more options that can be used to predict software effort estimation based on COCOMO, such as machine learning. In this paper, we compare machine learning algorithm with tuning parameter method to know whether tuning parameter estimation is better than machine learning estimation or vice versa. In this paper, we use Random Forest Regression as machine learning algorithm to estimate the effort. We also compare it with another machine learning algorithm, Support Vector Regression, and Bee Colony Method as parameter tuning method. The results of experiment is evaluated by their error rate. The results show that Random Forest Regression is better than Support Vector Regression and Bee Colony Method.
A Probabilistic Approach to Committing Solar Energy in Day-ahead Electricity Markets

Grid-tied solar is governed by a variety of complex regulations. Since a higher solar penetration imposes indirect costs on the grid, these regulations generally limit the aggregate amount of grid-tied solar, as well as the compensation its owners receive. These regulations are also increasingly limiting solar's natural growth by preventing users from connecting it to the grid. One way to address the problem is to partially deregulate solar by allowing some solar generators to participate in the electricity market. However, day-ahead electricity markets require participants to commit to selling energy one day in advance to ensure system stability and avoid price volatility. Thus, to operate in the day-ahead market, solar generators must solve a solar commitment problem by determining how much solar energy to commit to sell each hour of the next day that maximizes their revenue despite the uncertainty in next-day solar generation. We present a probabilistic approach to addressing the solar commitment problem that combines a solar performance model with an analysis of weather measurement and forecast data to determine a conditional probability distribution over next-day solar generation outcomes, which we use to determine solar energy commitments each hour that maximize expected revenue. We show that, as the deviation penalty for over-committing solar increases, our probabilistic approach enables increasingly more savings than a deterministic approach that simply trusts weather measurements and forecasts.
Malicious Attack and Intrusion Prevention in IoT Network using Blockchain based Security Analysis

The Internet of Things (IoT) as a demanding technology require the best features of information security for effective development of the IoT based smart city and technological activity. There are huge number of recent security threats searching for some loopholes which are ready to exploit any network. Against the back-drop of recent rapidly growing technological advancement of IoT, security-threats have become a critical challenge which demand responsive and continuous action. As privacy and security exhibit an ever-present flourishing issue, so loopholes detection and analysis are indispensable process in the network. This paper presents Block chain based security analysis of data generated from IoT devices to prevent malicious attacks and intrusion in the IoT network.
WITHDRAWN: Weblog Fuzzy Clustering Algorithm based on Convolutional Neural Network

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
EsharaGAN: An Approach to Generate Disentangle Representation of Sign Language using InfoGAN

EsharaGAN is a Bangla Sign Digit generation model based on Information Maximizing Generative Adversarial Networks (InfoGAN). Augmenting the mutual information between latent variables and observational variables is the fundamental working principle of InfoGAN. This paper focused on generating disentangle representation of Bangla Sign Digit images using a variant of Generative adversarial network InfoGAN. Working on the IsharaLipi dataset, this model consists of 13 layer network architecture-input layer, dense layer, convolutional layer, transpose convolutional layer, activation and batch normalization layer which minimizes the loss function, computation power and generates non distorted images like the real ones. ReLU and Tanh is used as an activation function. This model provides an exceptional result as the inception score of the model is 8.77 which is remarkable for a generation model.
Speaker Recognition Using SincNet and X-Vector Fusion

In this paper we propose an innovative approach to perform speaker recognition by fusing two recently introduced deep neural networks (DNNs) namely - SincNet and X-Vector. The idea behind using SincNet filters on the raw speech waveform is to extract more distinguishing frequency-related features in the initial convolution layers of the CNN architecture. X-Vectors are used to take advantage of the fact that this embedding is an efficient method to churn out fixed dimension features from variable length speech utterances, something which is challenging in plain CNN techniques, making it efficient both in terms of speed and accuracy. Our approach uses the best of both worlds by combining X- vector in the later layers while using SincNet filters in the initial layers of our deep model. This approach allows the network to learn better embedding and converge quicker. Previous works use either X-Vector or SincNet Filters or some modifications, however we introduce a novel fusion architecture wherein we have combined both the techniques to gather more information about the speech signal hence, giving us better results. Our method focuses on the VoxCeleb1 dataset for speaker recognition, and we have used it for both training and testing purposes.
A Framework for Learning Cross-Lingual Word Embedding with Topics

Cross-lingual word embeddings have been served as fundamental components for many Web-based applications. However, current models learn cross-lingual word embeddings based on projection of two pre-trained monolingual embeddings based on well-known models such as word2vec. This procedure makes it indiscriminative for some crucial factors of words such as homonymy and polysemy. In this paper, we propose a novel framework for learning better cross-lingual word embeddings with latent topics. In this framework, we firstly incorporate latent topical representations into the Skip-Gram model to learn high quality monolingual word embeddings. Then we use the supervised and unsupervised methods to train cross-lingual word embeddings with topical information. We evaluate our framework in the cross-lingual Web search tasks using the CLEF test collections. The results show that our framework outperforms previous state-of-the-art methods for generating cross-lingual word embeddings.
Differential Evolution in Robust Optimization Over Time Using a Survival Time Approach

This study presents an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve robust optimization over time problems with a survival time approach to analyze its viability and performance capacity of solving problems in dynamic environments. A set of instances with four different dynamics, generated by two different configurations of two well-known benchmarks, are solved. This work also introduces a comparison criterion that allows the algorithm to discriminate among solutions with similar survival times to benefit the selection process. The results show that the standard DE holds a good performance to find ROOT solutions, improving the results reported by state-of-the-art approaches in the studied environments. Finally, it was found that the chaotic dynamic, disregarding the type of peak movement in the search space, is a source of difficulty for the proposed DE algorithm.
The potential use of local government planning data for soil contamination analyses

Contamination information is obtained at considerable expense through site investigations by developers in the UK. This field information is submitted to local government as part of the planning process. Local government, therefore, has a vast resource of site-specific contamination data for developments in their area. These data are focused on brownfield sites. They are in hard quality format with some data quality issues, particularly for older site investigations. This research study presents the results to access and analyse this data. The dataset is evaluated in terms of the information available, its form, quality and the potential use of this information for researchers, regulators and developers. A preliminary analysis of the data is presented in which the main contaminants in the study area were investigated and local anomalies such as elevated lead levels were highlighted. In addition, asbestos was found at low concentrations in 25% of made ground soil samples; a result requiring further study to inform future risk assessment. The potential use of this countrywide dataset is currently limited by the hardcopy format and storage of the dataset. Updating the submission to digital format could result in a vast national resource of brownfield information.
Augmented Reality and Quick Response Code Technology in Engineering Drawing Course

Augmented reality (AR) technology is a cutting technology. It can enhance the perception of reality and provide more information of the real world. AR technology is used in many fields, like medical, industry, and education. In this paper, AR is introduced into engineering drawing course. Engineering drawing course is difficult to learn as it is a knowledge that students rarely contact with. To have a close contact with the models and the views, a convenient AR application (AR app) is proposed in the paper. With the help of the AR app, virtual models and real views are combined well. The models can be moved and rotated by touching the screen. Some videos of model assembly and disassembly can be obtained when scanning the quick response code (QR code). Thus, it is convenient for students to use mobile phone to study engineering drawing effectively. The interactive teaching environment enhances students' spatial thinking and cultivates their sense of innovation.
Cultivating Ethics – A perspective from practice

Ethically responsible design of technology has been under prolific discussion in recent years. Since design practitioners play a big part in shaping technology, their understanding of ethics is important. Much research has been conducted on ethics in design, but research findings are rarely used in industry. Our study addresses this gap by creating a space for conversation for design practitioners to explore the meaning of design ethics and related challenges. Our findings confirm the complexity of integrating ethics into the everyday practice of design, and suggest co-creating possible solutions as a way to start cultivating ethos in practice. Practitioners envisioned ideas for a toolbox concept, thoughts on how to integrate ethics into existing practices, and a visual moral compass. Our findings translate into a set of questions aiming to make ethical issues in practice visible, and to stimulate discussions around what designers considered to be ethical challenges.
A Fully Automated System to Detect Malaria Parasites and their Stages from the Blood Smear

Malaria is a common disease caused by female Anopheles mosquitoes. There are mainly four species of malaria and each species has four stages. Early detection of malaria is a key factor in proper patient management as well as reducing death from malaria. The traditional way of detecting malaria is time-consuming and varies depending on the expertise of the pathologist. To avoid the person dependency for appropriate detection several studies implemented an automated method for detecting malaria parasites. The automatic detection of malaria parasites with their stages from blood smear has been proposed in this work. The blood smear images of malaria patients were collected from the online database. After applying some preprocessing U-Net was used to segment the RBC (Red Blood Cell) from blood smear images, CNN to identify infected RBC by malaria parasites, and finally, an award-winning neural network called VGG16 was used to recognize the different types and stages of malaria. The segmentation accuracy and specificity of the U-Net model were 97.67% and 92.05% respectively. The detection accuracy and specificity of infected RBC were 100% and 95% by using the CNN model. The average accuracy and specificity of the VGG16 model for malaria species detection 95.55% and 94.75% respectively. The average staging accuracy and specificity of different types of malaria parasites for the ring stage were respectively 96.25% and 94.82% by applying VGG16.
Intelligent Distribution Framework and Algorithms for Connected Logistics Vehicles

This study presents an intelligent distribution framework based on edge computing and proposes navigation and obstacle avoidance algorithms for connected logistics vehicles (CLVs) on the basis of Trimble BD982 positioning sensor and tentacle algorithm (TA). An edge computing framework for the distribution of CLVs is established, and the functions of three layers (cloud server, edge equipment, and terminal) are described in detail. The basic functions, hardware, and software systems of the CLV are designed and presented. Focusing on autonomous driving, a Global Positioning System (GPS) navigation algorithm and an obstacle avoidance control strategy on the layer of edge equipment are developed on the basis of the TA. Autonomous GPS navigation is realized by combining the entire road network with the local road network to avoid obstacles. The TA is improved to help the CLV for avoiding obstacles. Experiments show that the hardware system and designed algorithms of the CLV are effective. The tracking error on the straight-line track is within 3 cm, the change rate of longitudinal velocity is within 0.3 g/s, the change rate of tire side deflection angle is less than 1°/s, and the calculation time is shortened by 25% when the calculation time is 30 ms. These results indicate that the vehicle has good stability and performance during obstacle avoidance in real time, and the proposed algorithms are superior to traditional algorithms. The CLV can realize autonomous GPS navigation, with high navigation accuracy, reliable obstacle avoidance performance, and stable vehicle handling.
Study on Earthquake Prediction Model Based on Traffic Disaster Data

This paper collects data on the damage to the traffic system caused by earthquakes in China in the past two decades, and uses KNN algorithm, SVM algorithm, logistic regression algorithm, naive Bayes algorithm and decision tree algorithm to train the data, then establish earthquake prediction models. The paper introduces the process of preprocessing, modelling, evaluation, and visualization of disaster data. An earthquake disaster inversion model based on traffic data has been established, which can predict the earthquake intensity based on the relevant data provided by the traffic department. The prediction accuracy is relatively accurate, which is very helpful for earthquake prediction and rescue operations.
DISCERNER: Dynamic selection of resource manager in hyper-scale cloud-computing data centres

Data centres constitute the engine of the Internet, and run a major portion of large web and mobile applications, content delivery and sharing platforms, and Cloud-computing business models. The high performance of such infrastructures is therefore critical for their correct functioning. This work focuses on the improvement of data-centre performance by dynamically switching the main data-centre governance software system: the resource manager. Instead of focusing on the development of new resource-managing models as soon as new workloads and patterns appear, we propose DISCERNER, a decision-theory model that can learn from numerous data-centre execution logs to determine which existing resource-managing model may optimise the overall performance for a given time period. Such a decision-theory system employs a classic machine-learning classifier to make real-time decisions based on past execution logs and on the current data-centre operational situation. A set of extensive and industry-guided experiments has been simulated by a validated data-centre simulation tool. The results obtained show that the values of key performance indicators may be improved by at least 20% in realistic scenarios.
Towards Enabling Eye Contact and Perspective Control in Video Conference

Eye contact is an important part of in-person communication. However, in modern remote communication - video conference calls, non-verbal cues through eye contact are lost. This is because it is not possible to make eye contact in a video call; to make it look like a person is making an eye contact, the user has to look at the camera directly, but then this means that the user is not looking at the visual of the interlocutor on the screen. In our research, we aim to build a hardware and software system that helps the users to make eye contact in video conference and change their perspective, placing a moving camera behind a semi-transparent screen. The system keeps the position of camera right behind the remote user's eye position on screen tracked by a computer vision algorithm, so users will be able to make eye contact during the video call. We believe the system will lead to better user experience in remote conference calls. In this work, we share our motivation, design choices, the current progress, and a plan to evaluate the system.
An Overview of Robust Reinforcement Learning

Reinforcement learning (RL) is one of the popular methods for intelligent control and decision making in the field of robotics recently. The goal of RL is to learn an optimal policy of the agent by interacting with the environment via trail and error. There are two main algorithms for RL problems, including model-free and model-based methods. Model-free RL is driven by historical trajectories and empirical data of the agent to optimize the policy, which needs to take actions in the environment to collect the trajectory data and may cause the damage of the robot during training in the real environment. The main different between model-based and model-free RL is that a model of the transition probability in the interaction environment is employed. Thus the agent can search the optimal policy through internal simulation. However, the model of the transition probability is usually estimated from historical data in a single environment with statistical errors. Therefore, an issue is faced by the agent is that the optimal policy is sensitive to perturbations in the model of the environment which can lead to serious degradation in performance. Robust RL aims to learn a robust optimal policy that accounts for model uncertainty of the transition probability to systematically mitigate the sensitivity of the optimal policy in perturbed environments. In this overview, we begin with an introduction to the algorithms in RL, then focus on the model uncertainty of the transition probability in robust RL. In parallel, we highlight the current research and challenges of robust RL for robot control. To conclude, we describe some research areas in robust RL and look ahead to the future work about robot control in complex environments.
Multiclass Classification on High Dimension and Low Sample Size Data using Genetic Programming

Multiclass classification is one of the most fundamental tasks in data mining. However, traditional data mining methods rely on the model assumption, they generally can suffer from the overfitting problem on high dimension and low sample size (HDLSS) data. Trying to address multiclass classification problems on HDLSS data from another perspective, we utilize Genetic Programming (GP), an intrinsic evolutionary classification algorithm that can implement feature construction automatically without model assumption. This paper develops an ensemble-based genetic programming classification framework, the Sigmoid-based Ensemble Gene Expression Programming (SEGEP). To relieve the problem of output conflict in GP-based multiclass classifiers, the proposed method employs a flexible probability representation with continuous relaxation to better integrate the output of all the binary classifiers, an effective data division strategy to further enhance the ensemble performance, and a novel sampling strategy to refine the existing GP-based binary classifier. The experiment results indicate that SE-GEP can attain better classification accuracy compared to other GP methods. Moreover, the comparison with other representative machine learning methods indicates that SE-GEP is a competitive method for multiclass classification in HDLSS data.
Enforcing Linearity in DNN Succours Robustness and Adversarial Image Generation

Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all possible adversarial perturbations improve robustness against adversarial attacks. Beside exploiting adversarial training framework, we show that by enforcing a Deep Neural Network (DNN) to be linear in transformed input and feature space improves robustness significantly. We also demonstrate that by augmenting the objective function with Local Lipschitz regularizer boost robustness of the model further. Our method outperforms most sophisticated adversarial training methods and achieves state of the art adversarial accuracy on MNIST, CIFAR10 and SVHN dataset. We also propose a novel adversarial image generation method by leveraging Inverse Representation Learning and Linearity aspect of an adversarially trained deep neural network classifier.