Reviewerly/exHarmony huggingface dataset:
Collaborative Filtering Recommendation Algorithm Based on Multifactorial Clustering

Recommendation systems applications have been applied to many fields and they are urgent need to better exploit. Collaborative filtering is a widely used recommendation technique. In collaborative filtering recommender systems, users are requested to provide ratings to the items they have purchased. By analysis of the ratings, systems can recommend items that are likely to be of interest to the users. However, it is not perfect yet. A few problems need to be solved such as sparse data and low recommended accuracy. In this paper, a novel collaborative filtering recommendation algorithm based on multifactorial clustering is presented. Firstly, two similarity measurements are defined according to user attributes and item category. Then, the linear combination of the two similarity values is applied to construct the user adjacency matrix. Secondly, in order to narrow down the search range of the nearest neighbors for the target users, we use Kmeans and differential clustering algorithm to group the users. By this doing, we can apply user correlation indices to predict scores within the group. Finally, the predicted score is weighted based on the scoring and attributes. That is to say, the impact of multiple factors is fully considered, such as rating information and attributes. The proposed method is evaluated using the Movielens dataset. Diversity experimental results demonstrate that the proposed method has outstanding performance in prediction accuracy and recommendation comprehensive performance.
An automatic cluster-based approach for depth estimation of single 2D images

In this paper, the problem of single 2D image depth estimation is considered. This is a very important problem due to its various applications in the industry. Previous learning-based methods are based on a key assumption that color images having photometric resemblance are likely to present similar depth structure. However, these methods search the whole dataset for finding corresponding images using handcrafted features, which is quite cumbersome and inefficient process. To overcome this, we have proposed a clustering-based algorithm for depth estimation of a single 2D image using transfer learning. To realize this, images are categorized into clusters using K-means clustering algorithm and features are extracted through a pre-trained deep learning model i.e., ResNet-50. After clustering, an efficient step of replacing feature vector is embedded to speedup the process without compromising on accuracy. After then, images with similar structure as an input image, are retrieved from the best matched cluster based on their correlation values. Then, retrieved candidate depth images are employed to initialize prior depth of a query image using weighted-correlation-average (WCA). Finally, the estimated depth is improved by removing variations using cross-bilateral-filter. In order to evaluate the performance of proposed algorithm, experiments are conducted on two benchmark datasets, NYU v2 and Make3D.
Implementation of Fuzzy Logic Controller with Different Membership Function Curves

Abstract Generally, the applications of power electronic are widely applied in the industrial, household, renewable energy fields, etc. Fuzzy Logic Controller is considered as an effective support control in the application because this implementation is very useful. Fuzzy Logic Controller is more reliable when compared to conventional control systems. This paper provides a Fuzzy Logic Controller based control method to identify the transformation of the membership function curve by using the dsPIC30F4012 microcontroller. In the implementation, the different membership function curves are adopted to obtain the maximum power output. The fuzzification process is needed to convert numerical numbers into fuzzy numbers. The other process is also required to convert the result of fuzzification process, it is called defuzzification process. To obtain the control system performance, the rule is defined in the rule base matrix. The experimental results were done to verify the analysis of Fuzzy Logic Controller.
Attributes guided facial image completion

Recently, facial image completion has witnessed substantial progress due to the increasing power of deep convolutional networks and generative adversarial nets. These methods try to reproduce the pixels in the missing regions of a facial image so that the completion results are indistinguishable from the original one. However, due to the multimodality of image contents, there are often many plausible ways to complete a facial image, which are equally good and meaningful. A user might be specifically interested in obtaining a particular completion result with certain desired attributes. To address this need, we propose a novel deep learning approach to facial image completion with multiple controllable attributes (e.g., gender and smiling). First, an attribute classification loss is proposed to guarantee the completed image should have some desired attributes. Second, our approach introduces a perceptual loss to model high-level semantic structures in a facial image, which greatly reduces structure distortions in the large missing regions. Third, an adversarial loss is employed to ensure the completion results are visually realistic. The three components cooperate well in our approach, forming an effective framework for high quality facial image completion with multiple controllable attributes. Experimental results on the CelebA datasets show our method not only achieves excellent performance on facial completion task, but also allows users to obtain specific completion results with facial attributes of interest.
Managerial Challenges in VR and AR in Asia

Virtual reality and augmented reality technologies have been widely adopted in tourism and hospitality, retail, communication, social media, and other fields. They have become effective tools to deliver consumer experiences and also are cost-effective. Though the adoption of these technologies has received support from customers, there are over-arching issues which have to be addressed for these technologies to diffuse into everyday lives. This chapter discusses how virtual and augmented realities are applied in various industries and the resulting immediate challenges which are needed to be addressed by the managers for a successful adoption.
Strengthening Enforcement in a Comprehensive Architecture for Privacy Enforcement at Internet Websites

This paper extends previous work to strengthen the enforcement portion of a comprehensive architecture for enforcing privacy when a user needs to submit personal data to an Internet website in order to obtain goods or services. Our extension proposes to use a website’s P3P privacy policy (derived in an automated way from its internal XACML access control policy) as a public key to encrypt the user’s data using IBE (identity-based encryption) technology. The website will only acquire the corresponding private key to decrypt this data if a trusted 3rd-party auditor (acting as an IBE private key generator) has verified that the P3P policy is an accurate statement of the site’s internal privacy practices. We discuss all the components of this model and describe our proof-of-concept implementation which demonstrates that such an architecture is feasible in real-world scenarios.
A recommender system for antimicrobial resistance

We have developed a method for prediction of antimicrobial resistance by representing the antibiogram and associated metadata in the matrix form, determining parameter of matrix factorization, i.e., the number of the latent factors, carrying out matrix factorization of the initial association matrix and construction of the new association matrix containing predictions of the associations between multiple samples antibiogram and associated metadata.
Feasibility and Uncertainty Analysis of Constitutive Parameters Identification for Composite Materials Using Displacement Field Data

Abstract Due to the normalized average strain error is remarkably higher than the displacement error, a new cost-effective method based on the Levenberg-Marquardt method, which is a gradient-based optimization method, was presented in this paper. The unknown constitutive properties are determined through minimization of the square difference between the reconstructed DIC-measured and the FEM-calculated displacements. The feasibility analysis of this method is presented by synthetic displacement data. The covariance matrix of the extracted material constitutive parameters has been given explicitly. It has been proved that this method can significantly reduce the uncertainty of the extracted material constitutive parameter induced by the approximation error due to reconstruction algorithm and the measurement noise. The material constants show strong robustness to the measurement noise. And the optimization procedure is not sensitive to the formulation and parameters in the initial approximation of the constitutive model. A sensitive matrix was derivation and evaluation iterative in this method to improve the convergence of the optimization procedure.
Research on Sentence Similarity Calculation Based on Attention Mechanism and Sememe Information

Focusing on the research of sentence similarity calculation, this paper proposes a method combining bidirectional long short-term memory networks, attention mechanism and sememe (BILSTM-ATTENTION-SEMEME) to achieve better results on semantic representation and in-depth understanding of the semantic level, consequently better resolve the problem in the aspect of semantics in the field of intelligent customer service. This method first solves the semantic representation problem through a model based on bidirectional long short-term memory networks and attention mechanism (Bilstm-Attention), then combines the sememe information of HowNet in the training of word vectors to improve the performance of semantic under-standing. Experimental results show that the proposed method is effective in the computation of sentence similarity in the field of intelligent customer service, and it can well combine the sememe knowledge of HowNet with the deep learning model based on attention mechanism. Compared with the baseline system, the accuracy rate increased by 6.5%.
Optimal Parameter Prediction for Secure Quantum Key Distribution Using Quantum Machine Learning Models

The advent of quantum computing is bringing threats to successful operations of classical cryptographic techniques. To conduct quantum key distribution (QKD) in a finite time interval, there is a need to estimate photon states and analyze the fluctuations statistically. The use of brute force and local search methods for parameter optimization are computationally intensive and becomes an infeasible solution even for smaller connections. Therefore, the use of quantum machine learning models with self-learning ability is useful in predicting the optimal parameters for quantum key distribution. This chapter discusses some of the quantum machine learning models with their architecture, advantages, and disadvantages. The performance of quantum convoluted neural network (QCNN) and Quantum Particle Swarm Optimization (QPSO) towards QKD is found to be good compared to all the other quantum machine learning models discussed.
A System for Cultivating Exploration Skills by Presenting Clues Based on the Analysis of Page Selection Behaviors

Activities performed on the Web have becoming increasingly more complicated. In such activities, the importance of Web exploration through trial and error has increased. To satisfactorily conduct such exploration, it is important for users to comprehend the process of Web exploration and reflect on their search intentions when constructing queries. However, such tasks are difficult for inexperienced people. In this research, we focus on searchers' behaviors of page selection in the Web exploration process. On the basis of behavior analysis, we have developed methods to extract the clues so that unskilled searchers can become aware of the evaluation against pages they acquired and utilize them in further searches. We develop a novel mechanism for cultivating exploration skills by presenting clues in accordance with a searcher's degree of Web exploration experience. In this paper, we describe the design of a support system based on our methods and the characteristics of our prototype with some snapshots.
Semi-supervised learning for classification on Chinese drug treatment questions

Construction of automatic Question Answering (QA) system for online healthcare community gains great attention from the researchers due to the lack of practitioners who can respond precise answers to patients' consultations. In this paper, we focus on drug treatment question classification task, which can help building QA system for such questions. Due to the lack of labeled data and the high cost of labeling work, we consider using documents or texts from the internet which are related to our task. We fetch a large amount of unlabeled question-answer pairs and drug instructions from the internet and design a co-training style method (which is a semi-supervised learning method) to utilize them. Using some specified models as classifiers, we prove that the classifier trained under our method outperforms the same model trained under supervised learning with less labeled data as input.
Intelligent photovoltaic panels based on solar tracking

The transformation of solar energy into electricity using photovoltaic cells has many economic and environmental benefits. As this technology reduces dependence on non-renewable energy sources, many developed and developing countries near the equator are increasingly willing to adopt such energy generation techniques. Its tilt angle with the horizontal plane and its orientation determines the photovoltaic panel (PV) yield. Thus the solar energy obtained from traditional photovoltaic panels can be maximized by replacing them with Intelligent photovoltaic panels. The Intelligent Photovoltaic panels adjust its orientation as well as tilt angle automatically with changing sun direction to receive maximum possible solar energy on its surface. A handful of studies on the orientation of tilt angle and its optimization have been conducted to improve the yield of photovoltaic panels; however, no solution has been proposed to estimate the optimum value of the parameters based on real-time monitoring.
E-Voting and e-Recap Verification and Validation Schemes for Indonesia Utilizing Cryptographic Hash Function Message Authentication Codes (MAC) and Public Key Infrastructure (PKI)

Conventional elections have many shortcomings, some of them are the calculation of a real count that takes a long time, the big costs of printing ballot papers and distribution costs both from the center to the regions and vice versa, the possibility of ballot paper shortages during the election, the possibility of fraud occurs because one person votes more than once, et cetera. Seeing this, the use of e-voting technology in the general election process is believed to be able to make the election effective and efficient. This paper aims to propose an e-voting verification and validation scheme that can solve these problems that might occur in conventional elections and still fulfill the election principles of Indonesia, namely Direct, General, Free, Confidential, Honest, and Fair. The e-voting system is equipped with an e-recap system which has a function for ballot verification so that all of the people can see, examine, and control the results of this e-voting system. This e-voting and e-recap scheme utilizes the application of cryptographic hash function Message Authentication Codes (MAC) and Public Key Infrastructure (PKI) with the aim is in the recapitulation results, there is not listed who the voters are and the votes given so that they still fulfill the confidential principles in elections, but all votes can be collected and verified. The voters themselves can check the vote that has been given, so that there is no vote modification when entering the e-recap system. Thus, the election can be transparent, accountable, and can be examined by the public. It is expected that with this verification and validation scheme on e-voting and e-recap, conventional elections can be replaced with an e-voting system, but still fulfill the election principles of Indonesia, such as Direct, General, Free, Confidential, Honest, and Fair. In addition, the election can still be transparent, accountable, and can be examined by the public.
Configurational Approach to Identify Concept Networks in selected Clinical Safety Incident Classes

Classifying clinical safety incidents (CSI) in their correct classes depends on the multiple concepts used to describe them.Machine learning based classification case study presented in this paper shows that it fails to identify the underlying complex concepts associations between the CSI classes.Two pairs of classes, each having high and low confused classes (as determined by the classifier), were further investigated by applying the set-theoretic-based logical synthesis methodology.The aim is to identify the relationships between concept networks for selected classes.The concept networks were identified using a set of 117 terms and measures taken included degree-centrality and in-betweenness centrality.In this study, using deterministic configurational approach, it is feasible to draw a meaningful relationship between concepts using the complex medical dataset sourced from the Incident Information Management System.The study is proof of concept that it is possible to identify concept networks and concept configuration rules for CSI classes.
Research on Massive Video Image Processing based on Cloud Server

In order to solve the problems in security field such as public security, traffic and anti-terrorism, which require high real-time performance of massive video analysis and computation, large amount of data and many analysis dimensions, this paper designs a cloud load balancing algorithm, scheduling strategy and real-time analysis for massive video processing by studying distributed parallel computing, cloud computing, intelligent video analysis and massive data processing technologies. A cloud server system for real-time analysis of massive video is also developed. The reliability and practicability of the system are verified by the deployment and application of the actual project.
Foreign Language Teaching and Intercultural Communicative Based on Network Environment and Cloud Computing Platform

Cloud computing platform, also known as cloud platform, refers to services based on hardware and software resources, providing computing, network and storage capabilities. Cloud computing platforms can be divided into three categories: storage cloud platforms based on data storage, computing cloud platforms based on data processing, and integrated cloud computing platforms based on both computing and data storage processing. With the rise of the network is changing all the traditional teaching methods and teaching idea; development of multimedia computer and network communication technology, provides an ideal cognitive tool for English learning environment, can effectively promote the learners to improve their English level. At the same time also contribute to the all-round development of students. This paper analyzes how to use the cyber source of teaching content, enrich the teaching methods, improve teaching effectiveness.
STATISTICAL TESTING TECHNIQUE FOR COMPARISON MACHINE LEARNING MODELS PERFORMANCE

The statistical testing technique is considered to compare the metrics values of machine learning models on a test set. Since the values of metrics depend not only on the models, but also on the data, it may turn out that different models are the best on different test sets. For this reason, the traditional approach to comparing the values of metrics on a test set is often not enough. Sometimes a statistical comparison of the results obtained on the basis of cross-validation is used, but in this case it is impossible to guarantee the independence of the obtained measurements, which does not allow the use of the Student's t-test. There are criteria that do not require independent measurements, but they have less power. For additive metrics, a technique is proposed in this paper, when a test sample is divided into N parts, on each of which the values of the metrics are calculated. Since the value on each part is obtained as the sum of independent random variables, according to the central limit theorem, the obtained metrics values on each of the N parts are realizations of the normally distributed random variable. To estimate the required sample size, it is proposed to use normality tests and build quantile– quantile plots. You can then use a modification of the Student's t-test to conduct a statistical test comparing the mean values of the metrics. A simplified approach is also considered, in which confidence intervals are built for the base model. A model whose metric values do not fall into this interval works differently from the base model. This approach reduces the amount of computations needed, however, an experimental analysis of the binary cross-entropy metric for CTR (Click-Through Rate) prediction models showed that it is more rough than the first one.
Personalized Privacy Protection with Spatio-Temporal Features in Social Networks

After users publish the social data, the privacy information was available to an attacker through node re-identification. In real life, users have different privacy protection requirements. Due to the personalization of users' privacy needs, the protection technology of attacks with the same background knowledge is insufficient to meets users' privacy protection requirements. To solve the problem, this paper proposes spatio-temporal classification levels of social network data background knowledge, and provides corresponding protection methods for different background knowledge intensification of each level. Specifically, three levels of protection requirements are defined on the basis of the gradually increasing background knowledge of the spatio-temporal features of the attacker. The effectiveness of the framework is verified by a large number of experiments.
A Telespirometer for the Developing World

There are numerous examples in which the introduction of expensive medical equipment into the developing world fail for lack of a basic understanding of the device operation, lack of spare parts and poor maintenance. This paper describes the development of a Fleisch pneumotachograph and cellphone based telespirometer that can easily be built from “junk box” medical and electronic components available in the developing world. This approach should introduce a sense of local ownership to the project as well as encouraging participation by the local electronics repair industry. Experimental results confirm that the forced expiratory flow data are reliably modulated onto an audio signal and transmitted by cellphone to a base station for examination by a district nurse or doctor. Flow measurement and data transmission accuracies are sufficiently good for remote diagnoses of chronic obstructive pulmonary disease.
Application Research of Parallel Frequent Itemset Mining Algorithm in Book Recommendation

Based on the traditional FP-Growth algorithm, this paper designs an improved Spark FP-Growth algorithm and deploys the improved serial algorithm to the Spark cluster to realize the parallelization of the algorithm. In this paper, the IPFP algorithm is used to mine frequent itemset from the library borrowing database and generate association rules. Then the generated association rules are used for the library's book selection recommendation service, which realizes the transition from the traditional book-oriented to the user-oriented, thus improving Work efficiency enhances the initiative and individuality of book selection.