Reviewerly/exHarmony huggingface dataset:
The Construction of Portrait Identification Tracking System Based on Mask R-CNN

A portrait identification and tracking system with strong real-time performance, good flexibility and controllable cost is designed and implemented in the paper. Firstly, Mask R-CNN neural network is used to extract the features of the target, and the COCO dataset is used to train and establish the portrait data model. As a result, accuracy of the portrait recognition is improved. Then, a portrait tracking system including monocular camera, data acquisition module, data processing module, steering gear and control system is built. And the "Raspberry Pi" control method is used to control the MG955 steering gear group. Finally, the recognition and tracking of characters can be realized through wired, WIFI, Bluetooth and other ways, which improves the universality of the system. The designed system has simple structure, complete functions and can be used for automatic aiming and tracking of other objects. The modular system can also be used for unmanned aerial vehicles, robots and other platforms.
Time and Domain Specific Twitter Data Mining for Plastic Ban based on Public Opinion

Every product available in our environment has a shelf-life, but plastic is the only material that is non-degradable. The complex polymer presents in the plastic make it durable and non-degradable. As a result, it found in different forms on the earth for a long time. People have become used to of plastic made product in day to day life like carrying bags, disposable cutlery, food packaging and many more. Extensive quantities of plastic waste have accumulated in nature and landfills and have posed an alarming hazard to the environment and have achieved the height of crisis level. Nowadays, India is amongst the high rated nations that produce a vast amount of plastic wastes and ranked four throughout the world. Though there is a law against the use of plastic in India the consumption of plastic made products is at the highest level as the restriction of usage is completely and effectively not implemented. In this paper, a model is proposed for analyzing the opinion of Indian population on the plastic ban with the help of sentiment analysis technique on Twitter textual data and train and test a machine classifier based on learning approach with several combinations of datasets achieving 77.94% classification accuracy. The result obtained will help to understand how effective and successful polybags ban scheme will be when entirely implemented in India.
Development of a Medical Social Robot

The article is devoted to the development of a socially assistive robot for healthcare institutions. The task of delivering a certain subject not present on the robot itself is reviewed very closely. An algorithm for solution of the task is proposed. Private case solutions are reviewed, and a structure of hardware and software complex of the robot is proposed.
Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.
Extracting Semantics from Question-Answering Services for Snippet Reuse

Nowadays, software developers typically search online for reusable solutions to common programming problems. However, forming the question appropriately, and locating and integrating the best solution back to the code can be tricky and time consuming. As a result, several mining systems have been proposed to aid developers in the task of locating reusable snippets and integrating them into their source code. Most of these systems, however, do not model the semantics of the snippets in the context of source code provided. In this work, we propose a snippet mining system, named StackSearch, that extracts semantic information from Stack Overlow posts and recommends useful and in-context snippets to the developer. Using a hybrid language model that combines Tf-Idf and fastText, our system effectively understands the meaning of the given query and retrieves semantically similar posts. Moreover, the results are accompanied with useful metadata using a named entity recognition technique. Upon evaluating our system in a set of common programming queries, in a dataset based on post links, and against a similar tool, we argue that our approach can be useful for recommending ready-to-use snippets to the developer.
Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning for multi-modal data classification. In this study, we utilized intelligent K-means clustering based on anomalous patterns and intelligent K-means clustering based on histogram information. In this study, clustering is used to generate codebook candidates before the training process, while incremental learning is utilized when the condition to generate a new codebook is sufficient. The condition to generate a new codebook in incremental learning is based on the similarity of the winner class and other classes. The proposed method was evaluated in synthetic and benchmark datasets. The experiment results showed that the proposed multi-codebook fuzzy neural networks that use dynamic incremental learning have significant improvements compared to the original fuzzy neural networks. The improvements were 15.65%, 5.31% and 11.42% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively, for incremental version 1. The incremental learning version 2 improved by 21.08% 4.63%, and 14.35% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. The multi-codebook fuzzy neural networks that use intelligent clustering also had significant improvements compared to the original fuzzy neural networks, achieving 23.90%, 2.10%, and 15.02% improvements on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively.
Short Text Clustering Enhanced by Semantic Matching Model

With the popularity of social networks, short text clustering has become a more and more important task that is widely used. Short text clustering is a challenging problem because social network short texts are characterized by irregular words, a lot of noise, and sparse features. We propose a Short Text Clustering enhanced by Semantic Matching Model (abbr. to STCSMM). The STCSMM method applies the knowledge of the tagged text similarity task dataset to the short text clustering through the semantic matching model, thereby improving the effect of short text clustering. First, we train a semantic matching network on the data set of the text similarity task, where the network contains the feature extraction layer and the vector distance calculation layer. Then, we use the learned feature extraction layer to extract short text feature and use the vector distance calculation layer replaces the commonly used distance metrics in the traditional K-means algorithm, such as cosine distance, Euclidean distance and so on. Finally, the text features obtained by feature extraction layer are applied to K-means based on vector distance calculation layer. This improved K-means clustering (STCSMM) has better performance on the microblog text clustering dataset than some existing methods such as K-means clustering with LDA, LSI or average word embedding feature vectors.
Integrating Cyber Security Requirements into a Power Management System

Based on the evolving cyber security threat landscape in critical infrastructure sectors, companies are setting up cyber security programs to manage this critical infrastructure risk. The Power Management System (PMS) has come to depend upon Information Technology (IT) such as Internet Protocol-based networks and open system-based human machine interfaces (HMIs) which then need to comply with the company cyber security program requirements. One of the primary security controls is the physical separation of Industrial Automation & Control Systems (IACS) from IT enterprise systems. The drawback with this arrangement is the company cannot as effectively leverage the knowledge from the IT group to manage these technologies in the IACS. Additionally, IACS maintenance personnel (including electrical) need training in network and computer competencies to effectively manage these systems as well as the cyber security controls. Integrating the PMS network and systems with the overall IACS Operational Technology (OT) architecture enables effective application and maintenance of cyber security controls for the PMS infrastructure. For example, the PMS can use centrally managed user access credentials to manage access to the PMS. Additionally, the OT management procedures will ensure adds, moves, and changes to user credentials are properly managed. This paper will elaborate on the benefits of IT/OT integration and overcoming the challenges of integrating the company's cyber security requirements into the power management system.
Architecture-Driven Development of an Electronic Health Record Considering the SOAQM Quality Model

Service computing has been considered for many years a suitable solution to develop and integrate legacy systems. Considering the important role of services for integrating legacy systems in organizations, quality should be treated as a key issue. By observing previous works published in the literature, it is clear that there is a need for the development of a specific quality model for Service-Oriented Architecture based on the latest ISO/IEC 25010 standard. This article presents a SOA quality model, named SOAQM, multiple architecture views and design decisions for developing an electronic health record (EHR). Most often, quality aspects, architectural views and decisions are neglected during the EHR development, which leads to difficulties for future software maintenance. Even those studies which present the software architecture only describe one or two views, or even a multiple number of views in only one box diagram, which may be confusing for most stakeholders. The process proposed in this article to develop the EHR system includes definition of software architecture through architectural decisions, multiple views and UML and SoaML models. The set of architectural decisions defined to develop the EHR was guided by SOAQM. A technical evaluation was performed by a group of eight software specialists that analyzed the EHR process development through a technical report and then provided their opinions through questionnaires and interviews. As a result of this evaluation, the EHR development process guided by the SOAQM quality model is considered important as it regards quality attributes in software development since early stages.
Blockchain technology in the Philippines: Status, trends, and ways forward

The Fourth Industrial Revolution has prompted firms across the globe to take advantage of the key technologies including blockchain to ensure that their competitive advantage is maintained with respect to their peers in the network. Blockchain technology has been widely used to promote a system that allows digital information to be distributed only to those which information is intended. Aside from that, blockchain technology enables trust, reliability, and transparency, to name a few, to all its users within the network particularly due to the influence of cryptography. The Philippines is currently underway to catching up with the technological changes in order to improve, at least maintain, its status in the global competition. This paper is directed to provide the current state of blockchain technology and its trends in both the global and local context. Moreover, the position of the country with respect to the probable adoption of blockchain technology nationally is also explored.
A Student-Centric Prototype for Courses Recommendation in Universities' Students Portal

The development of Recommender Systems in universities students' portal provides a significant impact on the student study plan and grades. Such systems are more beneficial when they match the software interface usability goals. Henceforth, the students will be more likely to use them. To the best of our knowledge, there are no proposed prototypes neither research works for Courses Recommender Systems in universities students' portal. Hence, this paper will present a prototype proposal for such a system. The proposed prototype evaluation achieved 4.8643 on the Likert scale reflecting a system with high usability.
Potential of Deployment of Virtual and Augmented Reality in Emergency Management Training via an Exploratory Interview Study

This article presents the outcome of an exploratory survey aimed to detecting the strengths and weaknesses and the degree of acceptance of the use of virtual and augmented reality in training of confined or suspected pollution environments workers. Forty-five Italian professionals (public and private) that work in health and safety in workplace, in different roles, have been involved in the survey (e.g. trainers, augmented and virtual reality specialists, researchers, health personnel). The analysis of the survey showed that the use of augmented and virtual reality allows several training sessions in safety, teach workers to perceive the risk and manage the negative emotions that hinder the success of the work. These reflections helped us to hypothesize a training proposal with the use of augmented and virtual reality.
Sensor Control for Multi- Target Tracking in the Presence of Doppler Blind Zone

For multi-target tracking of unmanned airborne radar, tracking performance is seriously deteriorated by the presence of the Doppler blind zone (DBZ). Aiming at this problem, a sensor control method based on Cauchy-Schwarz divergence (CSD) to suppress the influence of DBZ is proposed in this paper. Firstly, the jump Markov model is introduced into the novel Gaussian mixture probability hypothesis density (PHD) filter. Secondly, the Cauchy Schwarz divergence expression between the predicted intensity and the posterior intensity of the filter is derived. Finally, based on Cauchy-Schwarz divergence, by controlling the trajectory of the unmanned aerial vehicle (UAV), the DBZ is avoided as much as possible, thereby improving the quality of measurements and the multi-maneuvering-target tracking performance of the unmanned airborne radar. Simulation results show that the proposed method can effectively suppress the influence of DBZ.
Development of Emotional Intelligence in Computing Students: The “Experiencia 360°” Project

This article presents the results of the multidisciplinary project “Experiencia 360°” developed at the School of Computing Engineering of the Costa Rica Institute of Technology (TEC). This project was developed applying an action research methodology that used games as a tool for strengthening socio-emotional skills (soft skills) with students in their third year of a university baccalaureate program. The methodology developed and the results achieved are presented. This project constitutes a first approach to the construction of a specific strategy aiming at developing socio-emotional skills in students following the Computing Engineering program, so that the results obtained can become the input for the development of a systematic process of multidisciplinary and innovative work that promotes students’ comprehensive education for their suitable professional incorporation in work environments.
Suitability of IoT to Blockchain Network based on Consensus Algorithm

The Internet of Things (IoT) and Blockchain are increasingly growing focus on research. Blockchain is like the Internet when we first knew of it. This new technology could revolutionize how we do everything. Seriously, everything. Think how the Internet changed our daily lives today. And just like that, we don't really have to understand the technology behind it and be knowledge of its importance. But what makes this innovation revolution a bigger deal than Bitcoin and possibly even the Internet itself, are the exponential opportunities the concept provides. However, the potential to integrated Blockchain to Internet of Things (IoT) has constraint due to the required high computational power. IoT comprises of numerous platforms which have limited performance. Usually, these platforms cannot handle intensive procedures and often has scalability issues. In this paper, we give an overview of consensus algorithms and the necessity of this method to blockchain technology. Moreover, we focused on three of the utmost common consensus algorithms used in blockchain technology and explore their potential adaptation in an IoT framework with respect to its requirements.
Design of a machine learning layer to optimize forgery detection in videos

AbstractAbstractWith the advent of so many YouTubers, the number of videos uploaded to the cloud is increasing at an exponential rate. Most of the video creators take advantage of the fact, that the videos uploaded are not checked for any kind of tampering, which is due to the computational and information limit over the cloud. Due to this, there is lot of fake-like videos over the internet, which misleads the viewers into believing whatever the video creator conveys. All this leads to a lot of chaos and misunderstanding for the viewers at large. To overcome this issue, this text proposes a novel machine learning layer which is computationally efficient and uses a two-layer processing scheme for forgery detection in large scale videos. With the help of this scheme, the system pre-learns the forgery types, and keeps a large learning set ready for speedy reference, this initially static set, is then dynamically modified based on the real time videos given to the system. This two-layer scheme allows for an improved end-to-end delay for the system, without compromising on the system security. Our evaluations on the REWIND dataset show that the proposed system gives more than 95% accuracy in terms of forgery detection, and reduces the delay by more than 50% when compared to other state-of-the art algorithms like key point selection, genetic algorithm and others.Subject Classification: 68T99Keywords: VideoforgerytamperingmachinelearningREWINDtwo-layer
Modelling Uranium in Vicinity of Groundwater Population by Neural Networks of Multilayers Perceptron

Abstract The existent of Uranium in vicinity of groundwater population can give a threat to the water supplier for human consumption. The objective of the research was to find the most important variables to the existence of the Uranium. This paper shows some modelling process for above matters by applying Neural Networks of Multilayers Perceptron. Data taken from US Department of Energy. Neural Networks used in this study were learning the representation of the model inside the data, and how best it relation with the output variable that we obtained from prediction. The results showed that the training samples was 87 out of 127, and the testing samples was 40 out of 127. The results were not giving indication that a mathematical model obtained. The conclusion was Conductivity becoming the most important variable to the existence of Uranium, which followed by the second importance that was Arsenic, the third importance was Selenium, the fourth important was Total Alkalinity.
Multispectral Photometric Stereo Using Intrinsic Image Decomposition

One of the main problems faced by the photometric stereo method is that several measurements are required, as this method needs illumination from light sources from different directions. A solution to this problem is the color photometric stereo method, which conducts one-shot measurements by simultaneously illuminating lights of different wavelengths. However, the classic color photometric stereo method only allows measurements of white objects, while a surface-normal estimation of a multicolored object using this method is theoretically impossible. Therefore, it is necessary to convert a multi-colored object to a single-colored object before applying the photometric stereo. In this study, we employ the intrinsic image decomposition for conversion. Intrinsic image decomposition can produce the intrinsic image which is not affected by the reflectance. Since the intrinsic image is the image with white object, we can obtain the surface normal by applying the conventional photometric stereo algorithm to the intrinsic image. To demonstrate the effectiveness of this study, a measurement device that can realize the multispectral photometric stereo method with seven colors is employed instead of the classic color photometric stereo method with three colors.
Compensatory Aggregation based Failure Aware Cloud Workflow Scheduling

Scientific and business application workflows modeled as directed acyclic graphs represent the complex computational problems, which are to be executed in distributed computing environments. Due to sharing, elasticity, complexity, heterogeneity, virtualization, and workload variations, the cloud computing systems offer a host of challenges in terms of performance and reliability. While running a complex workflow application in the IaaS cloud, failures may be triggered by its resource failures or workflow level failures, which lead to large-scale service disruption with adverse consequences. In some static task scheduling algorithms like earliest completion time scheduling (ECTS) and heterogeneous earliest finish time (HEFT), since resource availability and the probability of task completion are not considered, workflow execution delay increases makespan. The proposed compensatory aggregation based failure aware (CAFA) cloud workflow scheduling algorithm considers resource selection based on the availability influenced by CPU load and processor speed for good performance and the probability of task completion for reliability. A method of compensatory aggregation of criteria is used for scoring each resource and choosing the resource with the highest score. The performance of CAFA algorithm is much better in terms of reduced makespan compared to HEFT and ECTS algorithms and is also found to be more reliable.