Reviewerly/exHarmony huggingface dataset:
A survey of style transfer based on generative adversarial network

Generative adversarial network (GAN) has become a hot research topic in the field of image processing. As an unsupervised training model, GAN has been widely used in the field of computer vision, especially in image style transfer. The purpose of the GAN is to make the generator generate a false image, and the discriminator cannot tell whether the input image is the real image or the generated image. Compared with traditional network models, GAN model has these advantages in image style transfer: GAN is composed of two different networks, and the loss function is automatically learned by playing games with each other. GAN belongs to unsupervised training and does not need to annotate the data set, which saves a lot of work. In this paper, improved GAN models related to image style migration are summarized. Firstly, the principle and method of image style transfer based on convolutional neural network are introduced. Secondly, the status, principle and prospect of GAN are introduced, and the causes of gradient disappearance and mode collapse of GAN are analyzed in detail. On this basis, the principles, advantages and disadvantages of CGAN, DCGAN, CycleGAN and StarGAN V2 network models are introduced. Finally, it summarizes the current problems and future research directions of style transfer based on GAN.
Opportunities, challenges, and training needs in the use of VR in Higher Education and SMEs: The case of Cyprus and Finland

The use and adoption of VR solutions have increased rapidly due to recent technological developments. Over the last years, there have been many initiatives to develop and adopt VR in many different fields, including education and business. This paper describes the state-of-the-art of VR use in Cyprus and Finland, focusing on the use of VR technology in Higher Education (HE) Institutions and in Small to Medium Enterprises (SME). Interview sessions were conducted with 16 expert people from Higher Education and business to firstly identify the key technologies available and secondly to analyze the opportunities, strengths, challenges, and training needs concerning the adoption of VR. The analysis revealed essential considerations and valuable insights about the development, adoption, and promotion of VR solutions in Higher Education and the business sector.
SMod: Scene-Specific-Prior–Based Moving Object Detection for Airport Apron Surveillance Systems

The detection of moving objects in airport apron surveillance encounters serious detection defects, which reflects that the classification is biased toward the background. In this article, we propose a new method, scene-specific-prior–based moving object detection (SMod), to detect the moving object by the use of the prior knowledge specific to the airport apron. We find that the airport apron is made of concrete and has a special gray–white color distribution. Therefore, first, we use a dual-mode Gaussian distribution to fit the color distribution of the airport apron. Further analysis shows that this fitted distribution also reflects the probability of a detection defect, which occurs when the aircraft and occluded apron area share a similar color distribution. Based on this, second, we present a scene-specific prior model based on the dual-mode distribution, where pixels with a larger detection defect probability are more likely to be classified as the foreground, so that the prior model is able to bias the classification to the foreground. As a result, the detection defect in the airport apron can be compensated for. Third, the prior model is used in conjunction with an apron model and an object model to detect a moving object within a Bayesian framework. Next, we conduct comparison experiments on the airport ground video surveillance benchmark to verify the effectiveness of the proposed algorithm. Finally, we demonstrate the application of SMod in an airport apron surveillance system.
WinSet

Windows play an important role in modern buildings. Getting to know the window states, e.g., open vs. close, is an enabler of many smart city applications, such as energy conservation and emergency response. In this work, we collect the very first multi-modal (RGB, thermal, depth, LiDAR, and ultrasound) window dataset named WinSet at various distances and angles. Multiple window types and heterogeneous window states are considered, such as openness (open vs. close), human behind (with vs. without), and lighting (on vs. off). Although our WinSet dataset has many usage scenarios, we concretize two sample ones: (i) analysis of state distinguishability using different sensor modalities and (ii) algorithms to detect open windows. We believe sharing WinSet and its collection procedure with the engineering and research communities will stimulate many creative smart city applications.
Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.
Humanism and the Great Opportunity of Intelligent User Interfaces for Cultural Heritage

Abstract In the spirit of the modern meaning of the word humanism, if technology aims at the flourishing of humans, it is of the greatest value to empower each human being with the capability of appreciating culture, in an inclusive, individual-adaptive manner. In particular, in this brief chapter, the case is made for the opportunity that intelligent user interfaces can offer specifically in the area of culture, beyond the obvious infrastructural advantages we are all familiar with. Insight is provided on research aimed at the continuous personal enriching of individuals at cultural sites, approaching the ancient humanistic vision of connecting us to our cultural past, now made possible for all, not just for an elite.
Multi-language Text Detection and Recognition Based on Deep Learning

In order to improve the accuracy of rotated text detection effectively, we combine the network architectures with different functions to make up for the shortcomings of each network. First, rotate proposal regions to achieve scene text detection in any direction. We also enhance the features of important channels and weaken the features of non-important channels to effectively improve the accuracy of the overall network. In order to achieve multi-scale target detection in the scene, the chips of the same size are intercepted on different scales. Therefore, it can zoom in on smaller targets, and zoom out on larger targets. The experiments show that this method is competitive in accuracy with traditional architecture.
Designing a Transactional Smart Assistant in Indonesian using Rasa Framework

The use of chatbots in the industrial world is increasing along with the increasing number of smartphone users in Indonesia. Many companies take this opportunity to develop their chatbot services with the help of Natural Language Processing (NLP) and Neural Network (NN) technologies. In this study, the creation of an Indonesian-based chatbot is more emphasized on the questions and answer domain, complaint handling, and transaction support at “pulsa” distributor company. Chatbots must have the ability to understand natural language, provide accurate predictions of subsequent actions and responses, especially for Indonesian chatbots. We compared several pipeline configurations in building the Natural Language Understanding (NLU) model, namely the default Rasa NLU with DIETClassifier, pretrained FastText with DIETClassifier and Spacy Multi Language Model using FastAi’s ULMFit for intent classification. The formation of the intent classification based on the complaint data log, customer service chat history and company FAQ resulted in 216 intents. The evaluation results on the pipeline configuration show that the intent classification and entity extraction tasks are better using the pretrained FastText model with an F1-Score intent evaluation reaching 73.4% and entity extraction 83%.
The <i>Footprint</i> of Factorization Models and Their Applications in Collaborative Filtering

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization ( MF ) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent ( SGD ), alternating least squares ( ALS ), and Markov Chain Monte Carlo ( MCMC )). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top- N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.
Personalized question-answering over linked data

A Personalized question-answering system aims to provide tailor-made answers for each user's question according to his interests and preferences. Building such a system depends essentially on creating user profiles that represent actual user interests. In this paper, we present a personalized open-domain question-answering system based on large scale ontologies. Furthermore, we describe the main components of our system and the techniques used for handling user's questions in a personalized approach based on the user profile. To evaluate our system, we introduce a user-centered methodology based on the users’ feedback. The results show a significant improvement in users’ satisfaction when their profiles are used to personalize answers.
Hybrid Multiplicative Secret Sharing

A secret-sharing scheme is d-multiplicative if it allows the players to multiply d (rather than two) shared secrets (without recovering them) by locally converting their shares into an additive sharing of the product. In this work, the d-multiplicative secret-sharing (MSS) is extended to a hybrid MSS (HMSS), which is mainly designed for sharing d secrets against different access structures. A necessary and sufficient condition for n-player d-HMSS schemes to exist is presented. The condition is necessary for arbitrary (possibly inefficient or even nonlinear) secret-sharing schemes.
A Research on Using Artificial Intelligence Information Technology to Enhance the Effectiveness of English Language Learning ---- An example of learning college English

Recently, in the process of rapid socio-economic development, artificial intelligence information technology has begun to be applied in various fields and has made good progress, especially in the field of education, where the use of artificial intelligence information technology enables students to interact and communicate with the system during learning, participate in learning activities according to their own learning level, and make self-correction and practice with the deficiencies and problems fed back by system. The system allows students to practice and improve their learning effectiveness. Therefore, in order to improve students' learning effectiveness, the use of AI information technology should be actively utilized in college English teaching, guiding students to use AI information technology for independent learning and interactive communication, combining their basic English learning ability and learning objectives to participate deeply in interactive learning activities, mobilizing students' interest while improving their learning effectiveness and laying a solid foundation for the subsequent development.
Error Probability Mitigation in Quantum Reading Using Classical Codes

A general framework describing the statistical discrimination of an ensemble of quantum channels is given by the name quantum reading. Several tools can be applied in quantum reading to reduce the error probability in distinguishing the ensemble of channels. Classical and quantum codes can be envisioned for this goal. The aim of this paper is to present a simple but fruitful protocol for this task using classical error-correcting codes. Three families of codes are considered: Reed–Solomon codes, BCH codes, and Reed–Muller codes. In conjunction with the use of codes, we also analyze the role of the receiver. In particular, heterodyne and Dolinar receivers are taken into consideration. The encoding and measurement schemes are connected by the probing step. As probes, we consider coherent states. In such a simple manner, interesting results are obtained. As we show, there is a threshold below which using codes surpass optimal and sophisticated schemes for any fixed rate and code. BCH codes in conjunction with Dolinar receiver turn out to be the optimal strategy for error mitigation in quantum reading.
A Depth Map Generation Method for Path Planning of Mobile Robot

Abstract Recently, the unmanned mobile robots have received broad applications, such as industrial and security inspection, disinfection and epidemic prevention, warehousing logistics, agricultural picking, etc. In order to drive autonomously from departure to destination, an unmanned mobile robot mounts different sensors to collect information around it and further understand its surrounding environment based on the perceptions. Here we proposed a method to generate high-resolution depth map for given sparse LiDAR point cloud. Our method fits the point cloud into a 3D curve and projects LiDAR data onto the curve surface, and then we make appropriate interpolations of the curve and finally implement the Delaunay triangulation algorithm to all the data points on the 3D curve. The experimental results show that our approach can effectively improve the resolution of depth maps from sparse LiDAR measurements.
A Blockchain System For MOOCs And Credit Bank

Nowadays, the rapid rise of the application of blockchain technology provides an opportunity to solve the weaknesses in the field of MOOCs, such as the authenticity of learning achievement certification, the problem of low efficiency of credit conversion, and the digital copyright protection of online courses. In this paper, we propose a blockchain system for MOOCs and credit banks. The system includes an entirely new heterogeneous multi-chain blockchain network structure based on the Polkadot framework, as well as three specific smart contract solutions to achieve MOOCs learning achievement evaluation and credit exchange, copyright registration of course resources and digital certificate verification, and intelligent transaction of paid courses. The proposed system has been demonstrated as a promising candidate solution for establishing a fairer, healthier, and more efficient online education platform and credit bank system.
DNR: A Unified Framework of List Ranking With Neural Networks for Recommendation

In recent years, list ranking learning has attracted further attention in the field of recommendation. Most of the existing list ranking methods use traditional or deep learning methods to fit user-item interactions and output a personalized ranking list. These solutions mainly focus on a certain aspect of linear and nonlinear interactions when learning latent structures of users and items. In fact, linear interactions and nonlinear interactions coexist and play complementary roles in a recommendation system. Once any type of interaction is ignored, it will result in the loss of linear or nonlinear features, which will further affect the overall performance of the model. To address this problem, we propose a general framework, DNR, short for Deep Neural Rank. To jointly learn linear and nonlinear features, it uses both traditional and deep learning methods to fit user-item interactions. This is a flexible architecture that can not only be extended to the integration of various linear models and nonlinear models but also be simplified for pairwise learning to rank. This paper focuses on the feasibility of integrating matrix factorization (MF) and multi-layer perceptron (MLP) into the framework as linear and nonlinear methods. Eventually, we conduct extensive experiments on three data sets (Movielens100K, Movielens1M, and Yahoo Movies). The results show that our proposed DNR framework achieves a significant improvement compared to existing methods.
A Comparative Analysis of Neural Network-Based Models for Forecasting of Solar Irradiation with Different Learning Algorithms

In the smooth operation of standalone and grid connected solar power plants, solar forecasting plays an important role and develops bilateral contract agreement between suppliers and customers. An efficient forecasting technique will provide great help to grid operators as it will balance the electricity demand and generation. In this paper, two neural network models: cascade forward back propagation neural network (CFNN) and Elman back propagation neural network (Elman) are established with four different algorithms. The performance of model’s are being judged on the basis of mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R2).The meteorological data of two years from Delhi location are used for training, and one year data on the seasonal basis are used for testing the network. Elman back propagation neural network model perform better as a comparison to cascade forward back propagation neural network model. The average value of MAPE, RMSE, and R2 using the Elman back propagation neural network with LM algorithm is 11.38%, 22.65%, and 88.11%, respectively. The MAPE’s lowest value informs us about the forecasting model’s efficiency. The proposed neural network models have the least MAPE and RMSE used to forecast global solar irradiation.
Viscoelastic Fluid-Inspired Swarm Behavior to Reduce Susceptibility to Local Minima: The Chain Siphon Algorithm

We present a novel distributed robotic swarm algorithm inspired by the open channel siphon phenomenon displayed in certain viscoelastic fluids. Self-siphoning viscoelastic fluids are often able to mitigate the trapping effects of local minima in the environment. Using a similar strategy, our algorithm enables a robot swarm to mitigate the trapping effects of local minima in potential fields. Once a robot senses the goal, local communication between robots is used to propagate path-to-goal gradient information through the swarm’s communication graph. This information is used to augment each agent’s local potential field, reducing the local minima traps and often eliminating them. We perform hardware experiments using the Georgia Tech Miniature Autonomous Blimp (GT-MAB) aerial robotic platforms as well as Monte Carlo simulations conducted in the Simulating Collaborative Robots in Massive Multi-Agent Game Execution (SCRIMMAGE) simulator. We compare the new method to other potential field based swarm behaviors that both do and do not incorporate local minima fixes. The distributed algorithm generates self-siphoning behavior within the robotic swarm, and this reduces its susceptibility to local minima.
An evaluation method of the impact of an online teaching system on engineering students' satisfaction during the COVID-19 lockdown

Universities had to scramble to arrange and redesign their teaching strategies due to the pandemic caused by the COVID-19 outbreak during the first semester of 2020. A sudden shift occurred from on-site to remote teaching, accompanied by a change in students learning habits. Such changes were committed to accomplishing the expected outcomes and competencies for the courses in progress. Higher education practitioners deemed it necessary to assess the validity and reliability of the newly applied teaching and evaluation approaches to ascertain the extent of fulfillment of students' achievement. This study uses the analysis of variance (ANOVA) for assessing expectation fulfillment and outcomes of online teaching during the 2020 lockdown. This work compares the outcomes between remote learning during the confinement period and the previous semesters amidst on-site teaching. The results draw on the instructor performance under the lockdown redesigned environment and the students' perceptions of their learning outcomes. The procedure has been applied to the subjects Simulation (SIM), Applied Statistics (EDA) taught at the Faculty of Engineering of the Universidad de Piura (UDEP, Peru), and Strength of materials (RdM), imparted at the ETSI Caminos, Canales y Puertos of the Universidad Politécnica de Madrid (UPM, Spain). The findings show the impact on some students' expectancies by the change to remote teaching and some improvement opportunities for future online teaching scenarios.