Reviewerly/exHarmony huggingface dataset:
Designing Multiple ANNs with Evolutionary Development: Activity Dependence

We use Cartesian genetic programming to evolve developmental programs that construct neural networks. One program represents the neuron soma and the other the dendrite. We show that the evolved programs can build a network from which multiple conventional ANNs can be extracted each of which can solve a different computational problem. We particularly investigate the utility of activity dependence (AD), where the signals passing through dendrites and neurons affect their properties.
Design Estimation and IOT Based Monitoring of Roof Top Solar Photovoltaic System for Data Analysis

In this paper, a study is performed for designing of Solar Rooftop photovoltaic system for factory complex. The detail design analysis for the accurate design is presented. The suitability of proposed plant is analyzed using PVsyst software. Taking consideration of proposed design, 300 KW solar rooftop photovoltaic plant is installed. The data monitoring of installed solar plant is an essential part for accessing efficiency of plant with respect to estimated capacity. In this paper, implemented IOT model for cloud based data monitoring system is presented. The presented data analysis of this paper is helpful for commercial analysis of solar photovoltaic project.
An AHP-based Decision Support System for Personnel Selection for Manager Position in Businesses

Humanity is one of the most important resources for businesses.Because, with human resources, the data of the institution can be obtained and information can be produced by processing.Thus, human resources make the business a learning and dynamic organization and ensure its continuity.In enterprises, personnel selection (in terms of quantity or quality) is carried out within the scope of Human Resources Management.This selection process usually takes place when a group of decision makers evaluates the candidates according to some criteria and their own opinions.However, this situation prevents an objective and fair selection.For this reason, in this study, a decision support system (DSS) has been developed by using the Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, to ensure objectivity and to select the most suitable personnel for the job description.The said DSS provides the selection of the marketing manager among the personnel working in an enterprise.For this, the 10 employees working in the marketing department of the enterprise for the longest time were taken into account.When the results are examined, it is seen that the most qualified personnel can be selected successfully in cases where customer satisfaction, performance value, and number of projects are prioritized.
Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data

The data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. Association rule mining introduces the method to extracts the related data from the datasets using the performance metrics like support and confidence. Traditional algorithm uses this metrics which is restricted to common attribute format. This problem is addressed by using generic attribute format with frequent pattern mining. The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data. Association rule mining has analyzed with various parameters in single connectivity and multi connectivity rules. Social networking suffers various problem because of uncertain data arrived for processing which is analyzed with various efficiency related elements. The analysis and prediction are also compared with the machine algorithms like classification and clustering and so on. Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.
Soft Adversarial Training Can Retain Natural Accuracy

Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant improvement in their performance. It sparked an interest in their deployment for real-time applications. This process initiated the need to understand the vulnerability of these models to adversarial attacks. It is instrumental in designing models that are robust against adversaries. Recent works have proposed novel techniques to counter the adversaries, most often sacrificing natural accuracy. Most suggest training with an adversarial version of the inputs, constantly moving away from the original distribution. The focus of our work is to use abstract certification to extract a subset of inputs for (hence we call it 'soft') adversarial training. We propose a training framework that can retain natural accuracy without sacrificing robustness in a constrained setting. Our framework specifically targets moderately critical applications which require a reasonable balance between robustness and accuracy. The results testify to the idea of soft adversarial training for the defense against adversarial attacks. At last, we propose the scope of future work for further improvement of this framework.
Not All Entangled States are Useful for Ancilla‐Assisted Quantum Process Tomography

Abstract It is well known that one can extract all the information of an unknown quantum channel by means of quantum process tomography, such as standard quantum‐process tomography and ancilla‐assisted quantum process tomography (AAQPT). Furthermore, it is shown that entanglement is not necessary for AAQPT; there exist separable states that are also useful for it. Surprisingly, in this work we find that not all entangled states are useful for AAQPT; there also exist some entangled states that are useless. The realignment operation used in entanglement detection can be related to the question whether a bipartite state is useful for AAQPT. The relationship between them is derived and the process of extracting the complete information of an unknown channel by the realignment operation shown. Based on this relationship, examples of a two‐qutrit entangled state and a two‐qutrit bound entangled state are presented. Both of these two examples are entangled but they cannot be used for AAQPT. Last but not the least, experimental verification is been performed on the IBM platform.
Face Verification Based on Deep Learning for Person Tracking in Hazardous Goods Factories

Person tracking in hazardous goods factories can provide a significant improvement in security and safety. This article proposes a face verification model which can be used to record travel paths for staff or related persons in the factory. As face images are captured from the dynamic crowd at entrance–exit gates of workshops, face verification is challenged by polymorphic faces, poor illumination and changing of a person’s pose. To adapt to this situation, a new face verification model is proposed, which is composed of two advanced deep learning neural network models. Firstly, MTCNN (Multi-Task Cascaded Convolutional Neural Network) is used to construct a face detector. Based on the SphereFace-20 network model, we have reconstructed a convolutional network architecture with the embedded Batch Normalization elements and the optimized network parameters. The new model, which is called the MDCNN, is used to extract efficient face features. A set of specific processing algorithms is used in the model to process polymorphic face images. The multi-view faces and various types of face images are used to train the models. The experimental results have demonstrated that the proposed model outperforms most existing methods on benchmark datasets such as the Labeled Faces in the Wild (LFW) and YouTube Face (YTF) datasets without multi-view (accuracy is 99.38% and 94.30%, respectively) and the CNBC/FERET datasets with multi-view (accuracy is 94.69%).
CustFRE: An annotated dataset for extraction of family relations from English text

Meaningful Information extraction is an extremely important and challenging task due to the ever growing size of data. Training and evaluating automated systems for the task requires annotated datasets which are rarely available because of the great amount of human effort and time required for annotating data. The dataset described in this manuscript, CustFRE, is meant for systems that learn extracting family relations from text. Sentences having at least two persons have been collected from the internet. The texts are first processed using Stanford's NLP pipeline for basic NLP tagging. Next, a team of natural language processing experts annotated the dataset. All family relations among persons in the texts have been annotated, or a no_relation is annotated if no family relation between two persons can be inferred from the text. After annotation, the dataset was verified by an NLP expert for completeness and correctness. CustFRE contains in total 2,716 annotations. The dataset can be used by information extraction researchers as a benchmark for evaluating their systems, and can also be used for training and evaluating family relation extraction systems.
Shaping the Behavior of Reinforcement Learning Agents

With the advent of machine learning and agent-based approaches, behavior-shaping in environments composed of several autonomous entities has become a popular and active research field for the development of unique realistic behaviors. Realistic simulations have been particularly studied in the fields of crowd management, swarm behavior analysis and civilization simulation. In this study, we present a new dynamic rewarding approach for shaping the behavior of reinforcement learning agents in mixed (cooperative and competitive) multi-agent environments. The evaluation of the proposed rewarding approach is tested in a developed 3D environment of two groups of ancient Greek warriors fighting inside an octagonal arena, testing different agent behaviors in various scenarios. Interestingly, the results reveal that the trained agents’ behaviors vary based on the situations and the constraints of the environment, resembling realistic behavior variations.
Deployment of Multi-Tier Application on Cloud and Continuous Monitoring using Kubernetes

It is very much important in the present world to enable the website providing the services to be live and managed continuously to support the large number of customers. Companies need to run the website and also monitor the website continuously. The monitoring can be performed with the tools and it is also important to ensure that the scaling of the service is also important to manage the cost and the availability. The entire requirement can be fulfiled with the Kubernetes, a tool for container orchestration. This tools can communicate with the container runtime engine and perform the deployment of the containers known as pods that are completely monitored using the Kubernetes. The tool can enhance the setting up of the complex clusters with the different services connected using the network internally. Kubernetes is helpful to deploy, scale, monitor and relaunch the containers based on the requirement. The entire approach of the tool is applied to launch the website and database on the multiple slave nodes and connect them to the master node then control the implementation of the entire setup from the master node itself. This entire design and the architecture is deployed on the AWS cloud with the servers on the cloud using the Kubernetes.
Soft Computing Techniques to Identify the Symptoms for COVID-19

COVID-19 since its discovery and spread have caused major disruption in the regular operation of the industries, schools, universities, business, and hospitals and eventually to the national economy of many countries. It is a highly contagious disease and spreading among communities at a very fast pace. Early detection of COVID-19 infected patients and quarantining them are few possible measures to restrict its spread. Persons infected by COVID-19 demonstrate many symptoms. However, these symptoms are somewhat similar to some of the common diseases like a common cold, viral fever, and body ache which we generally face in our day-to-day life. Hence, most of the medical experts confuse COVID-19 with some very common diseases, and when the actual cause is known by that time the situation becomes worse. In this light, this study proposed a new concept based on soft computing techniques to determine the exact symptoms responsible for COVID-19.
The Internal Consistency and Accuracy of Automatically Scored Written Receptive Meaning-Recall Data: A Preliminary Study

Vocableveltest.org is a testing platform on which users can create on- line self-marking meaning-recall (reading or listening) and form-recall (typing) tests that address a number of limitations of the existing vocabulary level tests and vocabulary size tests. A major limitation of many existing vocabulary tests is the written receptive meaning-recognition (multiple-choice or matching) format which is associated with increased error due to guessing and decreased power to measure the type of vocabulary knowledge suitable for reading practice (McLean et al., 2020; Stewart et al., 2021a; Stoeckel et al., 2021), despite being designed for this purpose (Nation, 2012; Schmitt et al., 2020; Webb et al., 2017). Conversely, scoring meaning-recall tests by hand is labour-intensive, and the internal consistency and accuracy of automatically marked data are unknown. Thus, this study investigated the internal consistency and accuracy of automatically marked responses of 98 words from the fifth 100 most frequent words of English. This study tested for knowledge of high-frequency words as a more robust test of the marking system, as these words possess multiple-meaning senses, making their automatic marking problematic. Furthermore, the predicted limited range of learners’ knowledge of these 98 words was expected to result in data of a low internal consistency. However, the automatically marked data had a high internal consistency (Cronbach’s α = 0.868) and was 98% similar to human marked meaning-recall responses.
A 3D Grouped Convolutional Network Fused with Conditional Random Field and Its Application in Image Multi-target Fine Segmentation

Abstract Aiming at the utilization of adjacent image correlation information in multi-target segmentation of 3D image slices and the optimization of segmentation results, a 3D grouped fully convolutional network fused with conditional random fields (3D-GFCN) is proposed. The model takes fully convolutional network (FCN) as the image segmentation infrastructure, and fully connected conditional random field (FCCRF) as the post-processing tool. It expands the 2D convolution into 3D operations, and uses a shortcut-connection structure to achieve feature fusion of different levels and scales, to realizes the fine-segmentation of 3D image slices. 3D-GFCN uses 3D convolution kernel to correlate the information of 3D image adjacent slices, uses the context correlation and probability exploration mechanism of FCCRF to optimize the segmentation results, and uses the grouped convolution to reduce the model parameters. The dice loss that can ignore the influence of background pixels is used as the training objective function to reduce the influence of the imbalance quantity between background pixels and target pixels. The model can automatically study and focus on target structures of different shapes and sizes in the image, highlight the salient features useful for specific tasks. In the mechanism, it can improve the shortcomings and limitations of the existing image segmentation algorithms, such as insignificant morphological features of the target image, weak correlation of spatial information and discontinuous segmentation results, and improve the accuracy of multi-target segmentation results and learning efficiency. Take abdominal abnormal tissue detection and multi-target segmentation based on 3D computer tomography (CT) images as verification experiments. In the case of small-scale and unbalanced data set, the average Dice coefficient is 88.8%, the Class Pixel Accuracy is 95.3%, and Intersection of Union is 87.8%. Compared with other methods, the performance evaluation index and segmentation accuracy are significantly improved. It shows that the proposed method has good applicability for solving typical multi-target image segmentation problems, such as boundary overlap, offset deformation and low contrast.
C-GRAIL: Autonomous Reinforcement Learning of Multiple and Context-Dependent Goals

When facing the problem of autonomously learning to achieve multiple goals, researchers typically focus on problems where each goal can be solved using just one policy. However, in environments presenting different contexts, the same goal might need different skills to be solved. These situations pose two challenges: 1) recognize which are the contexts that need different policies to perform the goals and 2) learn the policies to accomplish the same goal in the identified relevant contexts. These two challenges are even harder if faced within an open-ended learning framework where potentially an agent has no information on the environment, possibly not even about the goals it can pursue. We propose a novel robotic architecture, contextual GRAIL (C-GRAIL), that solves these challenges in an integrated fashion. The architecture is able to autonomously detect new relevant contexts and ignore irrelevant ones, on the basis of the decrease of the expected performance for a given goal. Moreover, C-GRAIL can quickly learn the policies for new contexts leveraging on transfer learning techniques. The architecture is tested in a simulated robotic environment involving a robot that autonomously discovers and learns to reach relevant target objects in the presence of multiple obstacles generating several different contexts.
A Simple Yet Effective Layered Loss for Pre-Training of Network Embedding

Pre-training of network embedding aims to encode unlabeled node proximity into a low-dimensional space, where nodes are close to their neighbors while being far from negative samples. In recent years, Graph Neural Networks have shown groundbreaking performance in semi-supervised learning on the node classification and link prediction tasks. However, because of their inherent information aggregation pattern, almost all these methods can only obtain inferior embedding results in the pre-training of the unlabeled nodes. The margins between a target node and its multi-hop neighbors become hard distinguishable during node message aggregation. To address this problem, we propose a simple yet effective layered loss to combine with a graph attention network, dubbed as LlossNet, for pre-training. We regard the proximity of a target node and its two-hop neighbors as a unit (called a unit graph), where a target node is needed to be more closer to its direct neighbor than its two-hop neighbors. As such, LlossNet would be able to preserve the margins of nodes in the learned embedding space. Experimental results of various downstream tasks including classification and clustering demonstrate the effectiveness of our method on learning discriminative node representations.
Service-oriented information technologies in library processes and scientific communication

The world experience of libraries’ practical activities shows that in the conditions of rapidly developing information technologies, a service-oriented approach is required when determining the strategic development goals in the field of technologies focused on users, to identify their needs and provide them with the necessary services. To solve the problems of increasing the efficiency of information and library services and information support of science by introducing modern service-oriented information and communication technologies, the urgent task is to develop a model for the effective organization of library processes and support of scientific communications in the web environment. The model is based on the automation of library processes; development strategies for library web resources (catalogs, databases), including heterogeneous information (text, video, audio, data, etc.) and virtual services (reference and bibliographic, library, etc.); implementation of systems for assessing their effectiveness using web analytics tools and qualitative methods, usability testing practices, methods of organizing and managing web representations.
UAVs routes optimization on smart cities and regions

Unmanned Aerial Vehicles are becoming a common technology used on smart cities and smart regions, thus requiring optimization of its routes with crucial importance. In this innovative work, six objective functions are optimized in order to provide sets of non-dominated solutions, composed of routes with different characteristics. Realistic constraints are considered such as obstacles and areas in which drones could not pass through. A didactic case of study considering points of a graph is used in order to illustrate a smart city composed of different regions. Obtained solutions are analyzed using a state-of-the-art visualization tool, which guides the comprehension of harmony and conflicts between objectives.
Using Blockchain in Resolving the Challenges Faced by IIoT

The Internet of Things (IoT) is defined as the network of different interconnected devices and services for sharing information. In order to improve the comfort, productivity, and automation for the consumer, IoT-based applications are highly assertive. A high degree of privacy, authentication and recovery from various attacks are needed to implement an IoT-based environment. Being a revolutionary technology, blockchain is impacting industrial IoT (IIoT) in a variety of ways. With the advancements in Big Data, large amount of data is generated by various machines in the industries. These data are vulnerable to various cyberattacks. We receive unprecedented attention to the importance of data integrity, especially given the IoT that connect all smart machines together. Blockchain uses its own mechanism to effectively guarantee data integrity. Owing to factors such as lack of expertise, the fragmented delivery of knowledge and the complex existence of their supply chains, producers, and suppliers in the software industry, exchanging and using minerals, are frequently unable to carry out significant supply chain due diligence. This chapter aims to study and analyze the challenges faced by IIoT and how the use of blockchain technology can address these challenges.
An Adoption of Blockchain Technology in Agri Food Supply Chain: An Overview

Blockchain Technology (BT) is anticipated to bring a drastic change in the manner transactions are carried in the agri food supply chain (AFSC). BT provides transparent operations by avoiding trust-related issues in the whole supply chain (SC). In this paper, a systematic literature assessment is taken as the main methodology to analyse and survey the recent literature. The main aim is to be identified as the pros and cons of BT related to its adoption in AFSC. This research paper also indicates the reason behind the slow adoption of blockchain (BC) as well as hesitation regarding the implementation in the industries. There are many ongoing projects which adopt BT in their SC, but still, there are some obstacles which are needed to be sorted out before implementation. Due to the barriers and some difficulties, its popularity is not spreading as much between the farmers and the implementing authority. BT deserves much popularity in any SC because it has a better capacity to handle transparency, traceability, privacy, security, and many more characteristics. The key finding of this paper is the potential challenges in the adoption of BT in AFSC. This study has proposed the solution to the various issues in the implementation of BT in AFSC.