Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Reinforcement Learning Algorithms Applied to Medical Image Registration

Nowadays, several medical procedures depend on the comparison and combination of images obtained in different modalities (magnetic resonance, computed tomography, PET, among others). Image registration is a geometric transformation process to align two or more images. It is necessary to have robust algorithms to find the best parameters of transformation in order to achieve accurate registrations. Reinforcement learning allows to train an agent through direct environment interaction, to achieve a goal. In this work, a comparison of the performance of Q-learning and Deep-Q with its variants is presented. Brain magnetic resonance images are used in 2D domain considering rigid deformations. The comparison is based on the reward values, computing the Pearson correlation factor in monomodal registration and Mutual information in multimodal registration, obtained during the learning process. It is also considered an error measure between the target parameters and the achieved ones. Finally, a backup memory criterion is proposed to train the Q-Network methods. Experimental results show a successfully behavior in all cases, but performance is improved when the proposed criterion is applied.
Deformable Interactions to Improve the Usability of Handheld Mobile Devices

Handheld mobile device use is dominated by touch interfaces; keyboards and other physical inputs are disappearing. In parallel, there is a trend towards more sophisticated devices supporting complex use. While touch devices are usable, the user experience is not optimal for all people or across all tasks. Augmenting touch devices with deformable interactions can support usability. I identify open research questions: how to pair deformation and touch, what are design questions for augmented devices, and what benefits they can provide to people? I outline work to date, highlighting a deformable mobile phone case.
Design of Cooperative Interaction between Humans and AI Creatures through Reinforcement Learning

Currently, it is difficult for humans and AI agents to cooperate because the agent has incomplete intention understanding. In this paper, we propose the design of cooperative interaction between humans and AI creatures. As an experiment, two creatures learned to lift a heavy box in a virtual environment simultaneously. As a result, one of the creatures was able acquire the behavior of following the other creature automatically. We need to verify whether cooperation between humans and AI can be established through ongoing investigations.
Disaster Information on Mobile Applications in Indonesia Based On Android Using Prototype Method

Indonesia has 28 regions in the Republic of Indonesia Archipelago which are declared as areas prone to tectonic earthquakes, volcanoes and tsunamis. Among these are NAD, North Sumatra, West Sumatra, Bengkulu, Lampung, Banten, Central Java, and DIY in the south, East Java in the south, Bali, NTB, and NTT. Based on these facts, it can give an idea that the South East Java Province in particular has a high level of vulnerability when compared to other islands, when viewed from the total population density. Disasters can occur anytime and anywhere so people need to increase awareness, awareness, and preparedness, which is most at risk during the emergency response phase, where in that phase the situation is very conducive and the increasing hoaxes about data and information on disasters that spread in the community, along with the development of technological advancements, we need a mobile application that can provide the latest data and information routinely in the community. Referring to the design of mobile application designs that have been designed, in this study using the sequential algorithm method. With the sequential algorithm in this design, users can easily use this Android-based disaster information application, just by entering the keywords in the year of the disaster event, the location will be searched. The purpose of making this mobile application is to be able to provide data and information about disasters in Indonesia to all elements of society effectively and efficiently.
Forecasting the Level of Expert Knowledge Using the GMDH Method

The expert knowledge is a very important resource for the development of an enterprise, but due to its unpredictable nature, it also very difficult to manage. Due to the growing phenomenon of frequent employee turnover in companies, it is necessary to keep such knowledge in the company and then to forecast its level in order to determine the type of missing knowledge in the company for the implementation of further projects/orders. In this paper a model for forecasting the level of expert knowledge, which assumes the use of the GMDH (Group Method of Data Handling) has been proposed. The model consists of the following elements: (1) formalised, expert knowledge acquired which is stored in the knowledge base, (2) level of knowledge in the enterprise, as the result of the clustering of acquired expert knowledge using the Bayesian network, (3) the GMDH method. Finally this model is implemented in a real case study from the Research and development department of a manufacturing company.
Photonic Architecture of Entanglement Assisted Binary Quantum Tensor Product Codes

We propose an architecture of an error correction scheme for a quantum communication system using integrated optics. Our scheme corrects burst errors using entanglement assisted binary quantum tensor product codes with an interleaver.
Triple Pattern Join Cardinality Estimations over HDT with Enhanced Metadata.

In this work, we present HDT-stats, an extension to the HDT operations, to compute further metadata when evaluating triple patterns over RDF graphs represented with HDT. Then, we propose a novel model that relies on the HDT-stats metadata, as well as the distinct position of SPARQL variables, to estimate the cardinality of joins between triple patterns. Our preliminary results suggest that our approach produce smore accurate cardinality estimations than existing solutions.
A Depth Map Inpainting Method-Based Background and Texture for RGB-D Image

The rendering technology-based depth map can greatly reduce amount of data in encoding, decoding, and transmission. Similarly, this method is important for synthesizing virtual viewpoints. RGB and depth (RGB-D) image can be acquainted by Microsoft Kinect camera. But hole and noise problems for depth map must be inpainted and improved. In this paper, based on background of RGB-D image and frame difference, the depth map is inpainted. The experimental results show that our method can obtain the better depth map.
Implementasi UML Dan Grennfoot Untuk Membangun Aplikasi Pengucapan Bahasa Inggris

One of the materials in the English Language course is listening, namely the skill of hearing the pronunciation of English sentences. Learning listening must use native speaker or recording material so that the pronunciation is heard according to the original. If you use the course participants as a sound source, there is a high probability of mistakes in the pronunciation of words or sentences. Then an application is needed to help with course activities as tutors or native speakers in giving correct English pronunciation.&#x0D; In this research, English Pronunciation Application was built using the Greenfoot Application with the object oriented programming concept. This application is to help pronoun learning that can output audio when the object clicked. The development method uses SDLC, application modeling uses UML and testing using Black-box testing. The English pronunciation application is compiled into a .jar file so that it can be run on a computer with a Windows operating system that has JDK installed.&#x0D; The test results show that this English learning application can replace tutors, produce the right audio, and can replace native speakers. This can be seen from the results of the questionnaire showing 82%.
Analysis of an Analog Optical Neural Network

A single layer analog optical neural network based on a 44 reconfigurable optical processor is analyzed through simulation. Using a stochastic optimization algorithm and a synthetic dataset, the network yields a final accuracy of 95.6%.
Architecture of a Semantic Repository of Green Economy Data in South Africa

The green economy sectors of South Africa have generated and hold rich information. While quite a large amount of this data is distributed and accessible for re-use, it is likewise caught by poor information structures and save in legacy databases. On the other hand, it has been demonstrated that Semantic Web (SW) technologies can facilitate the extensive scale linking and sharing of distributed information source. Therefore, the aim of this project is to show how semantic web innovations can be adopted for the effective management, efficient dissemination, sharing and re-utilization of green economy information in South Africa. Consequently, the core of the architectural solution proposed in this study is completely based on SW technologies as opposed to the customary types of persistent information storage facilities, such as relational databases. Furthermore, ongoing advances in Semantic Web have prompted an expanding popularity of the Linked Open Data development. The semantic enabled architecture proposed in this research work will in addition promote the transformation of green economy information into RDF to make it available to a greater audience as Linked Open Data.
Approaches and Methods for Individual Performance Assessment in Information Systems Projects

Organizations have to manage human resources effectively, as these are fundamental to their success. Indeed, it is widely recognized that human resources have a direct influence in the performance of organizations. Therefore, organizational success is highly dependent on an adequate management of human resources. In this context, the performance assessment of people is crucial, as it is an important process for implementing efficient and effective motivational and rewarding systems. However, in the case of information systems projects, there is not much research work focused on human resources performance evaluation. This chapter aims to contribute to fill this gap by reviewing several approaches and methods for performance assessment, which can be applied to information systems projects. The presented approaches and methods are focused on personality, behaviors, comparison, and outcomes/results.
Chaotic Pilot and Synchronization Encryption of Optical OFDM System Based on Polar Code

In this paper, we propose a chaotic pilot and synchronization encryption scheme for optical orthogonal frequency division multiplexing (O-OFDM) system based on polar code. In the chaotic pilot encryption scheme, the pilot structure and the pilot interval are encrypted. In the proposed scheme, the chaotic sequences produced by the three-dimensional Lorenz chaotic system are used to generate the structure of the pilot and the interval of pilot insertion, which can improve the physical layer security (PLS) of the system. Meanwhile, the chaotic sequences are also used to complete the OFDM symbol time synchronization by encrypting the training sequence. In addition, polar code is adopted to reduce the bit error rate (BER) of the system and improve system performance. Compared with the traditional OFDM signal, simulation shows that the BER performance of the system has been improved, and ~8.2dB gain can be reached at BER of 10-3 after 60km standard single-mode fiber (SSMF) transmission.
Network Search for Binary Networks

Neural networks with both high accuracy and small network size are urgently required for mobile phone applications. However, previous network search methods do not take network size into account. In this paper, we use the reinforcement learning method to search for networks offline, with both high accuracy and small network size. Gaussian policy is used to explore the number of convolutional channels in a finer manner. Parameter reward is included in our reward function to punish large networks. We also use binary networks to further reduce network size. Without skip connections or branches, the network generated by our method is competitive with other methods on Cifar-10. Our network is much smaller than networks generated by other network search methods. Besides, our accuracy is higher than original binary network reported in BinaryConnect and is competitive with other real-valued networks.
A Machine Learning Approach for Hot Topic Detection in News

We explore the related problems of topic detection within the stream of news sources collected by newspapers aggregators. In this paper, we focus on evaluating the effectiveness of the collaboration between preprocessing techniques and document clustering techniques and propose to use Pearson product-moment correlation coefficient to address the relation between keywords for retrieving topics behind keywords. The proposed approach is an experiment on over 10,000 articles which has been labeled manually and the experimental result is superior to the state-of-the-art methods with respects to the preciseness.
FLONet: Fewer Labeling Cost Active Learning for Deep Neural Network

Active learning is a promising technique to reduce the images labeling effort, training time and memory usage in deep learning applications. Although active learning has proven its effectiveness in some aspects of deep neural networks, there are still some challenges. First of all, most of the current active learning researches learn knowledge from the output of the last layer while ignoring other relevant information like features; Secondly, the analysis of the informative samples is insufficient, which make the results difficult to be theoretically and intuitively explained and verified. In this paper, we propose FLONet, a Fewer Labeling cOst Active Learning neural network design for image classification. Compared to current active learning researches, FLONet is not only able to learn information about what kind of samples are rich informative, but can significantly reduce the overhead of samples labeling in the middle and late stages of training as well. Moreover, lightweight FLONet is proposed with less execution time and memory usage. From the selected informative samples, it could be observed that our method can filter out samples that are misclassified with relatively high probability. From the experiments, we tested on two public datasets and showed that this sample selection method can achieve state-of-the-art effects in terms of actively searching informative samples, and can reduce the overhead of samples labeling by the factor of 66% in FashionMNIST and 46% in Cifar10.
Adversarially Erased Learning for Person Re-identification by Fully Convolutional Networks

The generalization ability of deep person re-identification networks is subject to inadequate person data and occlusions. To relieve this dilemma, we propose a feature-level augmentation strategy, Adversarially Erased Learning Module (AELM), using two adversarial classifiers. Specifically, we utilize a classifier to identify discriminative regions and erase them to increase the variant of features. Meanwhile, we input the erased feature maps to another classifier to discover new body regions, which effectively resist occlusion of key parts. To easily perform end-to-end training for AELM, we propose a novel Identity model based on Fully Convolutional Networks (IFCN) to directly obtain body response heatmap during the forward pass by selecting corresponding class-specific feature map. Thus, the discriminative regions can be identified and erased in a convenient way. Moreover, to capture discriminative region for AELM, we present a Complementary Attention Module (CoAM) combined with channel and spatial attention to automatically focus on which feature types and positions are meaningful in the feature maps. In this paper, CoAM and AELM are cascaded into one module which is applied to the outputs of different convolutional layers to integrate mid- and high-level semantic features. Experimental results on three challenging benchmarks demonstrate the effectiveness of the proposed method.
A Study of Disparity Map Based on Improved Dynamic Programming Algorithm

In recent years, as a related technology, stereo display(3D) has been growing more and more rapidly with the development of internet. However, some problems have arisen, either. To solve the problem about holes and poor matching in the process of virtual viewpoint generation, a new method for improving the disparity map of Dynamic Programming algorithm (DP) is proposed. Firstly, the RGB images of the left and right viewpoints are color-divided in the Hue-Intensity-Saturation space (HIS), and then the images is stereo-matched by the proposed method that DP algorithm and the Sum of absolute differences algorithm (SAD) to generate an initial disparity map. The cost function based on HIS space is added to the calculation process of matching cost, which increases the precision of disparity calculation. Then the linear interpolation and median filtering are used for eliminating the holes in the disparity map. The experimental simulation shows that compared with the traditional algorithm, the improved algorithm optimizes the quality of the synthetic disparity map, and reduces the mismatching rate. In addition to these, compared with standard disparity map, the improved method preserves the information of the initial image to some extent.
Prerequisites for the development of a universal template of an interactive simulator for a foreign language at the University of Telecommunications

This article discusses the need to create an interactive simulator for a foreign language at the University of Telecommunications. The advantages of such a simulator for use in classroom and out-of-class work are given. The prospects of using a universal simulator in the educational process for students and teachers of the university are identified and presented. The article describes the interrelation of various university disciplines through a universal simulator, that is, it is shown that competent application of an interactive simulator leads to the integration of disciplines, which increases the level of education.
Tourism Service Recommendation Based on User Influence in Social Networks and Time Series

In the mobile network, user can share their trip stories at any time and place, the massive user data can provides an opportunity to mining users' travel preferences. Tourism recommendation has been studied by lots of researchers, although they took into account the social relation of users and the need of travelers, but if multi-dimensional social information and the seasonality of tourism service are considered, it may lead to better personality efficiency for tourism recommendation system. In this paper, we propose a novel personalized tourism service recommendation model termed GA-LSTM_CSInf, which fused multi-modality travel information include the multi-dimensional social information, time series and tourism service category. The main work is listed as follows:(1)we combined genetic algorithm (GA) and LSTM network for predicting the popularity and score of tourism service.(2) Considering the multi-dimensional social information of users in social networks, we proposed the Explicit Social Influence (ExSoInf) and the Implicit Social Influence (ImSoInf) of users, and then integrated them into SVD algorithm.(3) Calculating user preferences for tourism service categories using matrix factorization technology. And then, the calculation results of the three methods are weighted and summed to obtain a fusion tourism service recommendation algorithm (GA-LSTM_CSInf). We conduct a comprehensive performance evaluation for our fusion recommendation algorithm using real-world dataset collected from Yelp. Experimental results show that our method achieves significantly superior recommendation quality compared to other state of art recommendation methods.
A Face Recognition Workflow Based Upon Similarity Measurement

In order to combine with different feature extraction methods, in this paper, we propose a new method using similarity calculation between multiple features. We regard face recognition as a maximum-a-posteriori (MAP) problem and the de-pendency between different features is defined by a markov chain. We construct a matching similarity function T which helps us finding a better matching image. Experiments were tested using AR database and the results have shown that our recognition rate is higher, especially robust to small occlusion and noise.
TRAINING TECHNIQUE FOR SUBJECT-ORIENTED PROGRAMMING IN THE LABVIEW SYSTEM

The article proposes a methodological approach to the training of future teachers of physics and informatics in subject-oriented programming using the LabVIEW system. The methodological support for the introductory lesson for learning how to get started in the LabVIEW system is considered, and an example of laboratory work on the development of a virtual device for determining the additional resistance to the LED is given. Laboratory work consists of several parts: theme, goals, theory on physics for solving tasks in the current work, stages of work in the LabVIEW system, tasks to control knowledge and skills. The methodology for the training in subject-oriented programming includes, in addition to the laboratory workshop, tasks to design more complex virtual devices as part of undergraduate works. In these works, in addition to the development of one or more virtual devices, it is proposed to solve various research problems using these devices. These research tasks are intended to confirm known physical laws, derive various constants and reference data, build graphs of physical dependencies, etc.
Simultaneous transmission of classical and quantum information under channel uncertainty and jamming attacks

We derive universal codes for simultaneous transmission of classical messages and entanglement through quantum channels, possibly under attack of a malignant third party. These codes are robust to different kinds of channel uncertainty. We show these codes to be optimal by giving a multi-letter characterization of regions corresponding to capacity of compound quantum channels for simultaneously transmitting and generating entanglement with classical messages. Also, we give dichotomy statements in which we characterize the capacity of arbitrarily varying quantum channels for simultaneous transmission of classical messages and entanglement.
Leadership and Education for National Security: The Role of Educational Managers

This paper looked at leadership and education for national security: the role of educational management. This paper posits that leadership and education are vital tools to maintaining national security in Nigeria. The challenges to national security in Nigeria ranges from Boko Haram insurgency, Fulani herdsmen killings, kidnapping and adoption of school children, ethnic and tribal crises, among others. It was suggested that for maintenance of peace and security in the country, the educational managers must ensure that core subject areas in the school curricula are emphasized such as civic education, social studies and peace and conflict resolution studies as well as entrepreneurship and vocational subjects for self employment. The government on her part must ensure that unemployment rate is reduced.
Multi-strategy-based e-negotiation using web services

Negotiation is an iterative process where opponent and proponent exchange offers and counter offers with each other, with each iteration opponent can be modelled in more efficient way and an appropriate strategy can be selected for a particular opponent, i.e., by treating strategy as a dynamic component. This research focuses on web-service-based automated negotiation with decision-making capability about the strategy selection. Every strategy has certain applicability which needs to be established through experimentation and analysis.