Reviewerly/exHarmony huggingface dataset:
A Hybrid Neural Network-Genetic Programming Intelligent Control Approach

The proposed work aims to introduce a novel approach to Intelligent Control (IC), based on the combined use of Genetic Programming (GP) and feedforward Neural Network (NN). Both techniques have been successfully used in the literature for regression and control applications, but, while a NN creates a black box model, GP allows for a greater interpretability of the created model, which is a key feature in control applications. The main idea behind the hybrid approach proposed in this paper is to combine the speed and flexibility of a NN with the interpretability of GP. Moreover, to improve the robustness of the GP control law against unforeseen environmental changes, a new selection and crossover mechanisms, called Inclusive Tournament and Inclusive Crossover, are also introduced. The proposed IC approach is tested on the guidance control of a space transportation system and results, showing the potentialities for real applications, are shown and discussed.
Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions

In this work we design a narrative understanding tool TEXT2ALM.This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives.The methodology used to implement the TEXT2ALM system was originally outlined by Lierler, Inclezan, and Gelfond [13] via a manual process of converting a narrative to an ALM model.It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning.The effectiveness of system TEXT2ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015.This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks.We also illustrate that the TEXT2ALM approach generalizes to a broader spectrum of narratives.
Making synchronous CSCL work: a widget-based learning system with group work support

This paper presents a system architecture for synchronous mobile Computer-Supported Collaborative Learning (mCSCL). The paper builds on the notion of content-independent mCSCL by adding the technology support designed according to the contemporary design principles to support young learners. The specifics of the presented architecture are the authoring application that allows for reuse of interactive web-based educational application modules named “widgets”. Widgets communicate via the Group Communication module to deliver group work educational experiences. The Group Communication module consists of the three sub-modules: Message Forwarding Sub-module, Activity Management Sub-module and History Management Sub-module. The three sub-modules are designed to deliver synchronous mCSCL experiences by distributing communication messages to the subscribed groups and their members in a timely manner and by ensuring possible state reconstruction necessary for smooth learner transition between widgets and devices. Such an approach adds to the robustness of synchronous communication which is demonstrated by utilizing the system and the developed widgets in two case studies where early primary school children learn Mathematics in pairs and triplets. The presented system is able to deliver synchronous mCSCL activities including pairs and triplets in a dynamic early primary school environment.
Binary Grey Wolf Optimizer with K-Nearest Neighbor classifier for Feature Selection

Iteration number and population size are two key factors that influence the effectiveness of a certain feature selection algorithm. Randomly choosing these factors, however, might be an impractical approach that could lead to low algorithm accuracy. In this paper, we assessed the changes in the accuracy of Binary Grey Wolf Optimizer (BGWO) at varying a function of iteration number (50,100,150 and 200) and population size (10,20,30) in four benchmark datasets. The results generally indicate that there is an optimum iteration number (T) beyond which the accuracy of BGWO started to decrease. Similarly, it was seen that an optimum population size (N) exists, which yield a high average accuracy of the BGWO algorithm. The findings suggest that it is essential to optimize the iteration number and population size before the execution of BGWO.
Facial Recognition based Attendance System Using CNN and Raspberry Pi

This work aims to present face recognition solution using deep learning based facial recognition algorithm. Convolutional Neural Network (CNN) along with triplet loss function has been used to tweak the neural network weights in a way to make the vectors closer via distance metric. The 128-d embedding of each image constitute the feature vectors while K-Nearest Neighbors (KNN) model classifier has been used along with maximum vote count to classify face images. A varying dataset which has been used in this work is the DSU Dataset. The DSU Dataset has been generated locally at DHA Suffa University, Karachi, Pakistan. The implemented algorithm is developed on Python and ensures an overall efficiency of around ninety five percent which is then implemented on Raspberry pi hardware along with an addition of digital attendance management through email.
Disaster learning through a map-based mobile application: an evaluation of its readability and user satisfaction

Abstract Mobile technologies have become a key potential learning tool for the public in this digital era, particularly for young people. The Indonesian government, through its National Disaster Management Agency, has developed and launched a mobile application called InaRisk Personal to help citizens become more aware of disasters. This paper aims to assess whether this map-based application is effective and efficient as a disaster learning tool for senior high school students by evaluating its readability (efficiency and accuracy of map interpretation) and overall satisfaction with the application by surveying 361 students in West Java, Indonesia. This study also compared the application with printed disaster maps obtained from local governments to roughly examine its performance. The findings show that both measurements (readability and user satisfaction) were higher for the application. We also found that most students prefer to learn about disasters through cartographic visualization on the mobile application with suggestions for improvements compared to the printed maps.
Dynamic Programming Applications: A Suvrvey

Dynamic programming is a mathematical optimization first invented in 1950s and lived till our times to make optimizations and reduce complexity in several different fields like bioinformatics, Electric vehicles, energy consumption, medical field and much more as a proof of being a powerful technique. In this paper, the various fields and aspects in which Dynamic programming has a significant contribution are surveyed.
Robotic question support system to reduce hesitation for <scp>face‐to‐face</scp> questions in lectures

Abstract Encouraging students to actively ask questions during lectures is a formidable challenge that can be addressed through innovative use of information technology. We developed a robotic system that allows students in a lecture to collaboratively decide questions to be asked by a humanoid robot. To verify whether the system reduces hesitation to ask questions during lectures, 62 university students were divided into two groups, and each attended two different mock lectures on the Nobel Prize in Physics. Two lectures were conducted with and without the proposed system in counterbalanced order. Results suggested that students who were usually hesitant to ask questions during lectures became less hesitant to ask questions face‐to‐face when they could use the proposed system. Moreover, the perceived activeness in the lectures increased when using the system. Multiple regression analyses revealed that certain student actions, particularly tweeting and showing agreement with the questions posted by others, were correlated with an increase in perceived activeness.
Mulr4FL: Effective Fault Localization of Evolution Software Based on Multivariate Logistic Regression Model

Fault localization is indeed tedious and costly work during software maintenance. Studies indicate that combining both structural features and behavior characteristics of programs can be beneficial for improving the efficiency of fault locating. In this paper, we proposed a framework, called Mulr4FL, for fault localization using a multivariate logistic regression model that combined both static and dynamic features collected from the program under debugging. Firstly, the hybrid metrics data set, with both program structural features and behavior characteristics combined, is constructed by static program analyzing and dynamically tracing that runs with a designed metrics set. Meanwhile, the fault information of the legacy program is also obtained from the bug tracking system. Secondly, Bivariate logistic analysis is performed to filter the metrics that are significantly related to faults, and then with the selected metrics and their measurements, a multivariate logistic regression model was constructed and trained. Finally, based on the trained logistic model, we conduct the multivariate logistic analysis on the features of the evolved software and predict the buggy class methods. An empirical study was conducted based on a set of benchmarks that are used widely in program debugging research. The results indicate that the Mulr4FL can significantly improve the effectiveness of locating faults in contrast to 5 baseline techniques.
How can augmented reality improve the user experience of digital products and engagement with cultural heritage outside the museum space?

Abstract The purpose of this study is to understand how augmented reality is improving the user experience of cultural heritage and museums by making it more engaging and allowing the user to better remember the information relayed. Specifically, this study aims to better understand the use of augmented reality, and how institutions are making use of it by interviewing people involved in two apps that operate outside the museum space. This study can conclude that augmented reality has improved the information delivered and some aspects of the user experience. However, it is difficult to argue that augmented reality is completely improving the user experience of cultural heritage and museums.
Management and Monitoring of Blockchain Systems

Blockchain technology is based on a decentralized model, in which pairs collaborate and build trust on a corporate or public network. Each peer organization can be represented by one or more nodes and this network of nodes is used to broadcast transactions and reach consensus for each transaction submitted. secure data encryption and new transactions linked to previous ones make it nearly impossible to edit old records without having to edit subsequent ones. On the other hand, controlling more than half of the nodes in the network could allow Blockchain data corruption. However, adding a layer of oversight of each Blockchain node and the entire Blockchain network could ensure truly decentralized and robust operations.
Deep Hashing With Weighted Spatial Importance

Hashing method has been widely used in big data retrieval because of its low computational complexity. Most of existing hashing methods learn the final hash code from the semantic information of the whole image. However, different spatial regions of an image have different influences during the hash learning. To tackle this issue, we propose a new deep hashing with weighted spatial importance (DWSH) in this paper. Specifically, the proposed DWSH first utilizes a spatial attention model to learn the importance of different spatial regions in the original image, and then assigns different weights to these spatial regions according to their importance. The final hash codes are learned based on the weighted spatial information. In addition, two strategies are designed to utilize the spatial importance, including discrete weight strategy and continuous weight strategy, which weight the spatial information with discrete and continuous values, respectively. The results of extensive experiments conducted on three benchmark datasets show that the proposed DWSH method is superior to the state-of-the-art hashing method based on different evaluation protocols.
A literature review on the effects of 6-Dimensional virtual reality's sport applications toward higher presense

Virtual Reality (VR) is widely applied in various domains such as medical, training, education, entertainment, and etc. In relation to sport training, VR-based system is used by many athletes for various exercise such as cycling and rowing. The objective of sport training can be divided into two: improving athletes' performance or their fitness. In sport training, performance is measured by using a timer, heart rate device, breathing mask and many more, whereby athlete's fitness is measured by their stamina, strength and endurance. In the previous researchers' assessment, questionnaire is commonly used for analyzing purposes. From those researches, it was suggested that presence plays a significant role towards enhancing athletes' performance and fitness while undergoing the sport training. This paper covers issues that are related to presence, 6D, its effects to VR applications, and the methods of assessment in detail. From the findings, it is anticipated that when higher dimensional VR system and human sense are used, a realistic sense of presence will be felt which creates a more realistic experience. In addition, this paper also discusses how extra elements such as altitude, temperature, wind-effect and many more can affect the feeling of presence while using VR application. These extra elements would create a higher dimensional VR application. The aim of this study is to identify an effective combination elements or dimensions that maximizes a high-fidelity presence in VR-based sport training.
An Attachment Recognition Method Based on Image Generation and Semantic Segmentation for Marine Current Turbines

Marine current turbine (MCT) is an efficient device for the utilization of marine current energy. As MCTs operate underwater for a long time, marine growth will attach to the machinery. Therefore, it is essential to recognize attachment on MCTs, since an increase in attachment will potentially deteriorate the power generation quality. Semantic segmentation is a suitable technique to perform this task, which however requires a large amount of labeled data. To acquire sufficient data, a specialized image generation method without high manual cost is proposed. For precise attachment recognition on blurry underwater images, we propose an improved semantic segmentation network (C-SegNet); this network adopts multi-scale feature concatenation and transfer learning to enhance the quality of recognition results. Besides, we use weighted cross-entropy loss to make the network pay more attention to some difficult segmentation objects. In the inference phase, dropout is utilized to estimate the recognition uncertainty, and a precise attachment area ratio is computed. Experimental results confirm the effectiveness of the proposed method under a submerged scene. In addition, C-SegNet has better performance than other state-of-the-art segmentation networks.
On the profits of competing cloud service providers: A game theoretic approach

The main contributions of the paper are summarized as follows. We take an analytical approach in the sense that the quality of service and the price of service as well as the revenue, cost, and profit of a cloud service provider (CSP) can all be quantitatively available based on well established analytical models. We argue that the satisfaction of a customer includes two aspects, i.e., satisfaction on the price of service and satisfaction on the quality of service. We are able to derive a closed-form expression of the expected customer satisfaction of a CSP. We develop a non-cooperative game formulation for a competitive cloud computing market with competing CSPs. We discuss the market stability mechanism which creates interaction among the CSPs, give the best response of a CSP based on the other CSPs' strategies, mention the existence of the Nash equilibrium, and develop an algorithm to find the Nash equilibrium.
Role-Based Design of Conversational Agents: Approach and Tools

The wide adoption of conversational agents in delivering services to users asks for user-centric approach to design of the experience. We propose a Role-Based design approach, introducing 9 archetypal roles/purposes of conversational agents, resulting from case study research of conversational interfaces available in the market. These roles cover a range of behaviors (reactive, proactive) and features (relational, operational) that will enable the design of human-like, bi-directional conversational experience. We exemplify this approach through workshop sessions that involved stakeholders from financial industry in using the developed toolkits to envision financial service experiences enabled by conversational-agent.
Application of Improved Simulated Annealing Genetic Algorithm in Task Assignment of Swarm of Drones

In this paper, a mathematical model is established based on the characteristics of the task assignment of the swarm of drones. The simulated annealing algorithm is introduced into the genetic algorithm of multiple groups. The improved genetic algorithm is applied to solve the mathematical model established in drones assignment task. Experiments result show that the task allocation calculation time base on the simulated annealing algorithm is significantly reduced compared with the standard genetic algorithm.
Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records

Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health records (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.
SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.
Automatic Text Summarization Model using Seq2Seq Technique

Increasing acquisition of digitization over the information storing and processing in our daily lives has increased the demand of digitization in multiple facets including in investigation processes as well. In fact, for crimes involving computer systems requires the adoption of best practices for the process of evidence extraction from acquired devices from the crime scenes. Over the past years, summarization has become a topic of research. Various techniques of Natural Language Processing (NLP) enabling researchers to generate efficient results for a wide spectrum of documents. In the proposed work Seq2Seq Architecture with RNN is used to perform summarization tasks for documents. The nature of the summary is abstractive and allows the generation of internal meaning by the model itself. With refinement and continual work, this model becomes a strong foundation to perform summarization on longer and legal documents. The results are efficient summary generation and ROUGE scores in the range of 0.6 - 0.7.
Fingertips Interaction Method in Handheld Augmented Reality for 3D Manipulation

Augmented Reality (AR) technology enhanced real world environment with virtual information either in two-dimensional (2D) or three-dimensional (3D) format. AR requires the interactive techniques to be as intuitive as possible for the user to accept. The issues to be addressed are the tracking inaccuracy and occlusion problems. Therefore, a prototype that enables fingertip manipulation method in handheld AR interface is developed. This paper explains user ability to interact directly with the virtual 3D object and manipulate it using fingertip-based gestures in handheld AR. It is used to perform 3D object manipulation for users to naturally interact and manipulate the virtual 3D objects. The paper describes the development phase, AR application with fingertip-based manipulation method to manipulate 3D object. Gesture tracking device is attached to handheld device to track the user’s natural hand interaction. The result presents in this research is a new prototype to Ancient Malacca AR application with fingertip-based manipulation method for handheld AR interface.