Reviewerly/exHarmony huggingface dataset:
A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

Abstract High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.
Building Recognition in Air and Satellite Photos

The paper deals with the algorithms of building recognition in air and satellite photos. The use of convolutional artificial neural networks to solve the problem of image segmentation is substantiated. The choice between two architectures of artificial neural networks is considered. The development of software implementing building recognition based on convolutional neural networks is described. The architecture of the software complex, some features of its construction and interaction with the cloud geo-information platform in which it functions are described. The application of the developed software for the recognition of buildings in images is described. The results of experiments on building recognition in pictures of various resolutions and types of buildings using the developed software are analysed.
Secure Image Watermarking in a Compressed SPIHT Domain Using Paillier Cryptosystem

A secure solution to the problem of copyright infringement and content authentication is to carry out image watermarking in secure signal processing (SSP) domain. Homomorphic encryption is considered one such solution for image watermarking in this domain. The Paillier encryption is found to be suitable for image processing applications in general and for watermarking in particular. In this article, a detailed investigation is carried out by using Paillier cryptosystem for twelve different color images in a compressed domain. The compression of the host images is carried out by SPIHT (Set Partitioning in Hierarchical Trees) coding. The visual quality of the images post embedding and image processing attacks is assessed by using two full reference metrics, Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The performance evaluation of the Paillier cryptosystem vis-à-vis watermark application development is carried out by computing three benchmark metrics: number of pixels change rate (NPCR), unified average changing intensity (UACI) and encryption speed.
A Fall Detection System Based on Convolutional Neural Networks

The paper proposes a system to detect the accidental falls based on Convolutional Neural Networks (CNN). The original video is captured using a normal digital camera. For the preprocessing, the video is split into several consecutive frames and then subtract the background from each frame based on K-Nearest Neighbor (KNN) algorithm to extract the moving object. Some noise is reduced by eroding and dilating. The system chooses the VGG-16 network which is one of the models of CNN for action recognition. The preprocessed frames are used as input to the networks followed by a novel three-steps training phase. First, the transfer learning is applied to do the full training in the ImageNet dataset to learn a generic feature extractor. Then the fine tuning is implemented by retraining the network in the UCF101 dataset to learn features to represent human motions. Finally, the network is fine-tuned by retraining in the UR Fall Detection (URFD) dataset which contains 'fall' and 'not fall' features. After that, the system focuses on the binary problem of fall detection. The generality test and some evaluations of the system are stated at the end of paper.
A Story-Telling Social Robot with Emotion Recognition Capabilities for the Intellectually Challenged

In this paper, a story-telling social robot is proposed. The robot is able to modify the evolution of the story considering the emotions the audience is feeling. To do that, the robot uses the user’s emotion from his/her face. We have used a deep learning-based model to identify the emotion. This model was trained and tested on state of the art dataset. We also demonstrate that involving generated, realistic samples in the training process as a way of data augmentation does not benefit the model at all. The mentioned samples are generated by a state of the art GAN, which is able to translate neutral faces to a range of emotions.
Metacognition, self-efficacy, and mathematics learning achievement: A study based on gender differences

Abstract This research was conducted in 2018 in the 4th semester students of the mathematics education study program at the Catholic University of Widya Mandira Kupang. The aim was to determine the effect of metacognition and self-efficacy on mathematics learning achievement in terms of gender differences. The method used was multiple regression analysis. The results of this study indicated that both partially and simultaneously in male and female student groups all had a significant effect on mathematics learning achievement. The effect of metacognition and self-efficacy on mathematics learning achievement in female students was greater than that of male students. Of the two independent variables, self-efficacy had more influence on mathematics learning achievement than metacognition in both male students and female students. The results of this study indicated that male students' metacognition and self-efficacy need to be further improved. Self-efficacy needs to be given the most attention compared to metacognition in mathematics learning so that student mathematics learning achievements can be improved.
An Agent-based Approach to Physical Rehabilitation of Patients affected by Neurological Diseases

Neurological disorders such as strokes, dementia, or ABI, among others, represent a major burden on European and worldwide healthcare systems, and can be understood as an unmet clinical need, recognised as a global challenge. This work discusses how agent technology can contribute to increase the autonomy of patients affected by neurological diseases who require physical rehabilitation. This approach can reduce face-to-face rehabilitation and the associated costs. We propose an architecture composed of software agents that play different roles to support the rehabilitation process from the point of view of patients and clinicians. This architecture has been used to deploy a multi-agent system for the physical rehabilitation of patients affected by a stroke. The involved rehabilitation exercises can be automatically assessed, classified and supervised. The conducted performance tests allow to conclude that the system can provide feedback to the patients in real-time.
Question generation based on chat‐response conversion

Summary Today, thanks to the major breakthrough of sequences to sequences model in the field of natural language, most of the dialogue generation tasks are focused on generating more effective responses. However, the responses proposed by the chat‐bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. How to transform the chat robot from a passive reply to an active questioner has become an urgent problem. In this paper, a question generalization method with four types of question proposing schemes are designed, implemented, and tested to automate question generation process. The proposed system is controlled by a probability‐triggered multiple conversion mechanism to actively propose different types of questions. We embed our methods in the mainstream dialogue generation model and demonstrate its effectiveness in dialogue response generalization on a standard dataset. In addition, it achieves good performance in subjective conversational assessment.
LVLNET: Lightweight Left Ventricle Localizer using Encoder-Decoder Neural Network

Automatic localization of the left ventricle (LV) is an important preprocessing step in any further analysis or quantification of LV function. Also, LV localization is usually done manually by MRI operator to plan Cardiac Magnetic Resonance Imaging (Cardiac MR) acquisition which can be standardized and automated to reduce the operator's error. In this study, we propose LVLNET; an automatic left ventricle localization approach; which utilizes a lightweight encoder-decoder-like convolutional neural network (CNN). We evaluated our proposed method using three different and independent datasets. The proposed method has estimated the region of interest of the left ventricle with an accuracy of 88% covering more than 90% of the left ventricle voxels. Also, the median distance between the real and estimated centers was 1.12 [0.61–2.38] mm. With the reported results, it is shown that our proposed method had overcome most of the badly annotated images however, considering dynamic movement through series timeframes would boost the resulted accuracy.
The (Trans) Disciplinary Alternative for Design

People are more demanding about the opportunities offered by the globalized market. There is a wide variety of products and services that border exclusivity and that, due to the advancement of technology, are now within the reach of many. To meet this voracious audience, a new profile of professionals with differentiated backgrounds is emerging. The purpose of this theoric chapter is to bring transdisciplinarity as a scientific and cultural approach presented as an alternative and as a methodological and epistemological suggestion for transdisciplinary practice in the daily life of the designer. We emphasize that the solution of the itinerant chair, transdisciplinary coaching or the consolidation of a transdisciplinary design curriculum for technical, ethically responsible and environmentally conscious high standards are valid alternatives to the designer’s educational model. In order to incorporate the transdisciplinary nexus into the daily routine of design, one needs attitude, research and transdisciplinary actions in a continuous way.
Digital Twins of Open Systems

This report is devoted to a new area of physics of open systems. In this area, methods and technologies were created to provide cognition, scientific understanding and rational explanation of states and properties inherent in open systems. In doing so, these systems can be represented by hundreds, thousands, and tens of thousands of variables. On this basis, a multidimensional knowledge-centric analytics of open systems considered at their natural scale and real complexity has appeared. Presently, the formation of a new cyber-physical paradigm of systems research and development goes on. The report includes a review of possibilities for applying this paradigm to automatic generation of digital twins of open systems in complex subject matter areas.
Clustering of Scientist using Research Areas at Akdeniz University

Research areas at large universities with a student body of 50K+ often include a wide range of disciplines including Social Sciences, Natural and Applied Sciences, Health Sciences, Fine Arts and Athletics. In order to increase collaboration and determine areas of strength requires correct clustering of scientist and research areas within a research organization. Grouping of researchers based on department/program may not be the best approach as science is increasingly becoming more interdisciplinary [1]. Therefore, the purpose of this study is to introduce a data mining approach to cluster scientist by research areas using similarity measures at a large institute.
AI - Driven Self-Optimizing Receivers for Cognitive Radio Networks

Cognitive radios (CRs) based on reconfigurable radio frequency (RF) electronics are a key requirement for implementing next-generation dynamic spectrum access (DSA) algorithms that improve management of the congested sub-6 GHz wireless spectrum. Suitable CRs incorporate adaptive components such as tunable notch filters, matching networks, and dynamic beamformers that can be intelligently tuned by RF scene analysis and situational awareness algorithms. Here we propose CR receivers that use machine learning (ML)-based modulation recognition (MR) algorithms for wideband real-time monitoring of spectral usage. The proposed systems enable detection and avoidance of anomalous signals. They also increase channel capacity and wireless data rates by exploiting white spaces in both licensed and unlicensed bands. An artificial intelligence (AI)-driven single-channel CR receiver prototype operating around 3 GHz has been implemented and tested. Experimental results show i) good over-the-air MR accuracy for several common modulation schemes using a deep belief network (DBN); and ii) autonomous self-optimization of the tunable RF front-end.
Museum Informatics: A Case Study on Augmented Reality at Tanjung Balau Fishermen Museum

Museums exhibit old artefacts with the aim to preserve them and allow visitors to learn about the artefacts that are mostly extinct or not currently available anymore. However, the exhibits are mostly static and cannot be explored further by the visitors. In tandem with the growth of various technologies in daily life, a lot of benefits could be obtained when Information and Communication Technology (ICT) is incorporated in a museum. This study aims to improve the visibility of a museum through museum informatics. Various applications have been proposed under the project in collaboration with the Tourism Division of Southeast Johor Development Authority (KEJORA) that manages Tanjung Balau Fishermen Museum. This paper focuses on the case study that applies augmented reality (AR) at one of the galleries in the museum. The study shows the collaboration yields many benefits that includes the exposure to the undergraduate students in solving real problems using AR technology and performing related research as part of their community service. The impact study reflects that the AR application meets the needs of the community that is the museum visitors, causes the change in knowledge, attitude, skill and aspiration; and encourages visitors to implement the change in term of their interest to visit museums with similar technologies.
SecRet: How to Apply the 5E Model for a Master’s Level Network Security Course

Engagement, exploration, explication, elaboration, and evaluation are the five stages of learning of the 5E's Instructional Model. We show that the 5E's main concepts can be successfully applied to a Master's level course in network security to provide a student-centered learning approach. We present the various techniques that we have used during the course, with a focus on active learning enhanced by technology usage. We provide examples, insights, and recommendations that are intended to help other educators. Our findings are general and can be applied in other contexts as well.
Impact of Spectre/Meltdown Kernel Patches on Crypto-Algorithms on Windows Platforms

We evaluate performance impact of kernel patches of two recently discovered catastrophic hardware vulnerabilities (Spectre and Meltdown) That can cause significant harm to security and privacy by memory leaks on a wide range of modern processors including Intel, AMD, and ARM processors and also can affect iOS and Android mobile devices. These vulnerabilities were publicly disclosed in conjunction in January 2018. There are multiple variants of exploits of these flaws. The only fix since their discovery barring designing new hardware is software patches. Such patches can negate some computing speedups computing that were gained by out-of-order execution and branch prediction. Since cryptography is the heart of data security, it is more important to study the performance impact of these patches on crypto-algorithms since exploiting these vulnerabilities can disclose secret keys and other confidential information within few minutes. We first explain these vulnerabilities and their exploits, and reviews relevant related work. Then, we report a number of conducted experiments on Windows platform with and without enabling the kernel patches while running various crypto-algorithms. The results are statistically analyzed to test the hypothesis of whether there is a significant impact on performance for various data sizes and types.
Damaged Image Restoration Based on Improved Criminisi Algorithm

Criminisi algorithm has been widely used to repair damaged images, but the algorithm still has problems such as unreasonable priority selection algorithm and high error rate of fixed sample block matching. In view of these existing problems, based on the Crminisi algorithm, this paper adds edge terms, improves the priority algorithm, and sorts the image filling order reasonably. Experiments show that the modified Criminisi algorithm can overcome the shortcomings of the image discontinuity after repair, and can improve the quality of broken image restoration.
Universal Boolean Logic in Cascading Networks

Computational properties of networks that can undergo cascades are examined. It is shown that universal Boolean logic circuits can be computed by a global cascade having antagonistic interactions. Determinism and cascade frequency of this antagonistic model are explored, as well as its ability to perform classification. Universality of cascade logic may have far-reaching consequences, in that it can allow unification of the theory of computation with the theory of percolation.
The curriculum design of lower-level tertiary public administration education in China: Challenges and redevelopment

Although China has witnessed the rapid growth of public administration (PA) education during recent decades, few studies have focused on the practical issues of lower-level tertiary PA education, namely, PA programs in vocational colleges. The methods that vocational PA programs can apply to improve educational quality are a theoretical gap since vocational PA education differs from university PA education. This article introduces the background and summarizes the characteristics and problems of vocational PA education. Based on the authors’ experience and the application of the Degree Qualifications Profile from other vocational programs, this article proposes a new method for comprehensively developing a curriculum. This method acts as a guideline and helps resolve the uncertainty of vocational PA programs.
Distance, classification and learning

This chapter is concerned with how we can characterise and recognise objects. This means we have to grasp a nettle here: a deep learning revolution has taken place in recent years and that affects computer vision very much. Naturally, a textbook is a time-controlled snapshot of its subject matter; by process, a textbook can be out of date when it is published. Bernard Shaw wrote that ‘progress is impossible without change, and those who cannot change their minds cannot change anything’ so we shall consider deep learning as a new way for feature extraction and one which has demonstrated that it is an awesome cannon in the arsenal of techniques for machine learning. Arguably, in deep learning the vast range of techniques that comprise computer vision are brought together, from low level to high level processing. We have previously considered it at the end of each section and now we bring this material together. But the revolution continues, so we only offer a snapshot here and one taken in 2019. We shall start by describing how we can collect together measurements for purposes of recognition and subsequently introduce distance measures that describe how different features appear to be by their measurements.
Measuring the Level of Plagiarism of Thesis using Vector Space Model and Cosine Similarity Methods

Abstract The purpose of this research is a computer system can assist in detecting and measuring plagiarism of scientific work quickly. There are many techniques that can be used to measure the level of plagiarism of a document. in paper, we will use the Vector Space Model and the research method used namely Cosine Similarity to measure the level of similarity of the thesis. The result of this method was comparing the level of thesis similarity with the dataset using the TF and TF-IDF techniques in graphical form. It can be concluded from the experimental process, using the TF-IDF technique produces a smaller value compared to using the TF technique.
Front-View Vehicle Make and Model Recognition on Night-Time NIR Camera Images

In this study, we propose a deep learning based vehicle make and model recognition method for weakly illuminated near-infrared camera images (NIR). Unlike earlier approaches that consider color images obtained in well-lit environments, this study proposes an approach for images captured at night-time. In the proposed approach, vehicle localization is carried out using single shot multi-box detector (SSD)model. Next, we utilize a convolutional neural network (CNN)based vehicle model classifier on the detected vehicle region. Data sets of different shade, light, reflection and other lighting effects have been created to be used in the training and testing stages of the proposed methods. Vehicle make and model classification model was tested using 3327 real-world night-time NIR images collected on a roadway. In order to observe the performance of the proposed vehicle make-model recognition method on enhanced images, three popular low-light image enhancement methods are also applied to our test dataset. Proposed model achieved 86% accuracy rate in model recognition and 95% accuracy rate in make recognition tasks.
Super-Resolution Based on Residual Dense Network for Agricultural Image

Abstract With the rapid development of convolutional neural network in image processing, its image processing performance is becoming more and more outstanding. With the development of agricultural informatization, our demand for effectively accessing to agricultural information is also increasing. For example, greenhouse image monitoring equipment for crops is only in a low level. In this paper, we propose a method to apply image super resolution to the processing of greenhouse monitoring images, and use residual dense network to enhance the learning of global and local features in agricultural images. Therefore, more detailed information can be obtained from the low resolution images. Experiments on the data sets are made from the greenhouse agriculture images, and experimental results show that our method can effectively enhance the quality of agricultural monitoring images. It can better meet the needs of agricultural monitoring.