Reviewerly/exHarmony huggingface dataset:
Based on Collaborative Filtering Personalized Recommendation for Online Learning

Nowadays, Internet technology has flourished and become a hot, infiltrating into all aspects of our lives. The education industry has also progressed with the innovation of technology, and online learning has emerged. Developers of online learning always put their own learning resources on the platform for the students to use. Students can learn by searching for content that they are interested in. But looking up in massive content may waste too much time. Therefore, the system can recommend different learning resources for different students or not through some historical browsing and the learning behavior or other contents becomes a important problem to be solved. This paper is aimed at this issue mainly to study the personalized recommendation of learning platform based on the students' learning behaviors.
Cscore

The development of deep learning advances the field of image processing. In recent years, lots of methods have made out- standing achievements in the domain of text-to-image synthesis, like Generative Adversarial Networks (GANs). Until now, although some evaluation metrics has been proposed to measure the performance of GANs in text-to-image synthesis, the quality of these evaluation metrics has always been controversial. At present, there is no widely used evaluation metric to judge the quality of generated image. In this paper, a novel No-Reference image quality evaluation metric is proposed, which can be used to get a score for each generated image produced by deep learning without referring to the real image. This evaluation metric can provide a new way to verify the quality of complex networks by judging the quality of generated images retroactively.
Real-time monitoring of changes in building tilt angle using double square artificial marks

To monitor changes in building tilt angles more accurately and efficiently and to safeguard the people's safety and property, a new tilt angle monitoring method was proposed in this paper. This method adopts double square artificial marks and a plumb line determined by MATLAB to record changes in the building tilt angle based on image processing technology. Two black square artificial marks with the size of 10 mm × 10 mm were placed near the side of the plumb line. A digital camera was adopted to collect tilt images which were saved in the computer. The tilt images were successively subjected to image greying, binarization, denoising, image segmentation and pixel calibration. Finally, the true length of a single pixel and tilt angle change value would be obtained. The experimental results showed that the accuracy of the method was 96% higher than the manual method. Compared with traditional manual methods and three-dimensional scanning monitoring methods, the advantages of this method are that the equipment used is relatively cheap, the image calculation workload is small and is suitable in dangerous working conditions. In conclusion, the proposed technique could accurately, concisely and effectively calculate the changes in building tilt angle and improve work efficiency.
LSOF: Novel Outlier Detection Approach Based on Local Structure

Many local outlier detection algorithms have been proposed inspired by the idea of local outlier factor (LOF). However, they often have low detection performance and are sensitive to neighborhood size because there is a major defect in their calculation formulas of outlier degree and the kNN (k-nearest neighbors) method is widely used to quantify a neighborhood of an instance. To address these issues, we define a novel nearest neighbors tree (NNT) to measure a neighborhood of an instance. Meanwhile, we propose a local structure outlier factor (LSOF), which score each local structure instead of each data point and report the top-scored local structures as anomalous local structures, where outliers and groups of outliers are easily divided according to characteristics of the NNT. Our experimental results demonstrate that the competitive behavior of our method on both synthetic and real-world datasets.
Decentralized Information Filtering Under Skew-Laplace Noise

Localization in large sensor networks requires decentralized computationally efficient filtering solutions. To model challenging indoor propagation conditions, including non-line-of-sight conditions and other channel variations, it may be necessary to consider non-Gaussian distributed errors. In this case, Gaussian filters cannot be considered as is and particle filters do not meet the system requirements on computational cost and/or available memory. In this article we explore decentralized Gaussian information filtering strategies under skew-Laplace errors, exploiting the hierarchically Gaussian formulation of such distribution. An illustrative example is considered to show the performance and support the discussion.
Analysis of Software Requirements Change Priorities Based on Complex Networks

Requirement change management is one of the most important process of software engineering. Requirements change occur frequently in open source community software projects. Because of the limited time and energy, it is important for developers of open source communities to decide which issues to be solved first. Understanding the priorities of requirement changes helps developers identify significant issues results in a better process of requirement change management, leading to a more dependable evolution of the project. In this context, a novel complex-network-based approach is proposed to analyze requirement changes with dependencies of requirements. First, the nodes of Associated Network of Requirement Change(ANRC) are issues of corresponding project, while edges are the dependencies of issues in the network. And then, corresponding weights are set according to different dependency types. The nodes in the network are later analyzed and sorted by centrality measures. In addition, temporal dimension is also considered.
Tackling Multiple Visual Artifacts: Blind Image Restoration Using Conditional Adversarial Networks

Restoring images that are degraded by visual artifacts like noise, blurness and other environmental visual artifacts like shadow, snow, rain, and haze is a challenging task. From literature, it can be seen that there are many model-based as well as blind restoration methods that have been proposed to restore an image degraded by a single artifact. In most practical cases, the image is degraded by more than one artifact. Complexity arises while trying to estimate degradation function using conventional techniques where images are degraded by multiple visual artifacts. To the best of our knowledge, there has not been any generalized method proposed to tackle this problem. In this paper, we propose a methodology using conditional adversarial networks for blind image restoration of images that are degraded by multiple artifacts. To analyze the performance, ISTD dataset (meant originally for shadow removal) is used by augmenting it with different types of noises and blurness. The network has been trained on this data and has been analyzed how it behaves during the addition of each artifact. Various image quality metrics like Peak signal-to-noise ratio (PSNR), Mean squared error (MSE), Structural similarity index (SSIM), Blind/reference-less image spatial quality evaluator (BRISQUE) and Naturalness image quality evaluator (NIQE) have been evaluated to validate the performance of the proposed method.
Hybrid-Key Stream Cipher Mechanism for Hadoop Distributed File System Security

Big Data is a term used to describe a collection of huge data that none of the traditional data management tools are able to store or process it efficiently. Big Data has been speeded rapidly in many domains such as, social networks and other information systems. One of the main challenges of Big data is how to keep sensitive information private and secure. So, data protection is considered the major problem of big data. In this paper, a hybrid─Key Stream Cipher Mechanism (HKSCM) has been introduced to improve the time of encryption and decryption data. This proposed mechanism consists of the One-Time Password (OTP) encryption algorithm and the Advanced Encryption Standard (AES) algorithm. The performance of our proposed HKSCM mechanism has been evaluated with respect to the execution time and file size. These have been done by a comparative study among the AES algorithm, RC4 stream cipher algorithm, hybrid algorithm, and our proposed HKSCM mechanism. According to the comparative results, it is found that our proposed HKSCM mechanism outperforms the AES algorithm, RC4 stream cipher algorithm, and hybrid algorithm by 14%, 3%, and 8% on average respectively. In addition, the read/write performance of the data protection in the Hadoop Distributed File System (HDFS) has been improved.
Applying Opinion Mining Technique on Tourism Study Case: Lake Toba

Lake Toba is the largest lake in Indonesia and the largest volcanic lake in the world. Lake Toba has unique cultures and area which makes the area as tourism destination either for local tourists or overseas tourists. Because of that, information related to Lake Toba is important for the development of Lake Toba tourism. These information are the tourists' opinion about hotel service, accommodation, transportation and the other things related to Lake Toba tourism. These information can be obtained from social media used by the society. Both society and tourists usually use social media for telling their experiences or for assessing something that is or has been done. As a result, it can be the most potential sources of information. By applying opinion mining technique and using social media twitter as a data source, the information can be gathered, processed and classified into three categories: positive, negative and neutral in order to be used by various stakeholders. This research gathers data from tweets in social media Twitter using Indonesian language and combines Lexicon Based and Naïve Bayes. Lexicon Based method is processed for labelling data training which will be used for classification in data testing. Naïve Bayes method is processed for calculating and classifying data testing into three categories: positive, negative and neutral. The result of this research is 82% the accuracy of data and 0.85 F-measure in classifying data correctly.
Open-Set Face Recognition in Video Surveillance: A Survey

Face recognition has received a substantial attention by the vision community over the past few decades. Most of the proposed frameworks have adopted the closed-set form of face recognition. However, when a novel unregistered face is presented to the system, the result will be misclassification. A more general and challenging open-set face recognition scheme is highly desirable due to its ability in dealing with the unknown persons which are not enrolled before. We observed that there is a shortage in survey papers that explore the research endeavors in open-set face recognition. In this paper, we present a literature survey of the open-set face recognition approaches that have been introduced for real-world scenarios focusing on video surveillance applications. Moreover, we discuss the current difficulties and suggest the promising directions for future research. The paper also describes the evaluation metrics and available benchmarking face video surveillance databases.
Enhanced QoS-Based Service Composition Approach in Multi-Cloud Environment

The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Composting accurate services have great attention to interest and importance for the Cloud consumer in the multi-Cloud environment. Therefore, an enhanced QoS-based Service Composition Approach in the multi-Cloud environment has been proposed to accurately compose the best Cloud providers to contract with them for composing the needed services to minimize the Cloud consumer cost function. In this paper, a modified Particle Swarm Optimization (PSO) has been employed to compose the best services based on the uncertainty of QoS attributes. The proposed approach has been implemented using a real QoS dataset. According to the comparative results, it is found that the proposed approach has achieved a high degree of optimality with low time complexity compared to the existing models.
A Scalable Cloud Native Platform For Interactive Museum Exhibits

Science museums with hands-on and interactive exhibits are on the rise. As museums grow, the need arises to have an online platform to allow visitors to continue their ex- perience beyond a day visit. In this paper, we first provide a brief survey of techniques for building scalable cloud native software frameworks. In order to achieve low cost and persist user data, we built a django application using Heroku and postgres. This platform can be scaled horizontally on-demand to handle highly variant user traffic for augmenting the museum experience. With a focus on educational experiences, a participant’s progress on activities at the museum are saved through an API we built and can be viewed on a website. Different activities at the museum generate data for the API which can be viewed anywhere. Simulated data was loaded into our framework to validate the efficacy of our solution. Future testing is outlined in collaboration with the Fleischmann Planetarium through a trial experiment with museum visitors.
An Analysis on Python Programming Language Demand and Its Recent Trend in Bangladesh

In today's world, we have a lot of programming languages. Which can realize our needs, but the important issue is, how to teach programming language in a very effective way to freshmen. Well, in that case, python can be a suitable language for both learning and real-world programming. It is a high-level, object-oriented programming language created by Guido Van Rossum was released in 1991. After python released day by day, it has become one of the most famous and demanding programming languages all over the world. In this paper will introduce and discuss python programming characteristics / features, organized syntax, and its powerful tools which help to solve many tasks also it is very close to simple math thinking. We tried to find out the recent trend/demand for python programming language in Bangladesh by operated a survey under faculty member from various universities, freelancer programmers and students from engineering studies. Also, make some simple comparison between python and other languages. From there we have figure out the most demanding features, characteristics of python language and the types of programming language supported by python. Python is now the most demanded and fastest-growing language which is founded by the support of researches done over many articles of various magazines and popular websites.
Sparse System Identification in Pairs of Pulse and Takenaka--Malmquist Bases

This paper considers the identification of a rational transfer function with sparse coefficients, under a pair of pulse and Takenaka--Malmquist (TM) bases and from a limited number of linear frequency domain measurements. We propose to concatenate pulse and TM basis functions in the representation of the transfer function, and prove the uniqueness of the sparse representation. We provide the sufficient condition under which the sparse system can be identified by $\ell_1$ optimization from random samples with high probability, and also provide the computation algorithm. Numerical examples show that the concatenated pulse and TM bases give a much sparser representation, with much lower reconstruction order compared to using only pulse basis functions and less dependency on the knowledge of the true system poles compared to using only TM basis functions.
A Novel Grey Wolf Optimization Based Combined Feature Selection Method

In data mining and machine learning area, features targeting and selection are crucial topics in the real world applications. Unfortunately, massive redundant or unrelated features significantly deteriorate the performance of learning algorithm. This paper presents a novel classification model which combined grey wolf optimizer (GWO) and spectral regression discriminant analysis (SRDA) for selecting the most appropriate features. The GWO algorithm is adopted to iteratively update the currently location of the grey wolf population, while the classification algorithm called SRDA is employed to measure the quality of the selected subset of features. The proposed method is compared with genetic algorithm (GA), Jaya, and three recent proposed Rao algorithms also with SRDA as the classifier over a set of UCI machine learning data repository. The experimental results show that the proposed method achieves the lower classification error rate than that of GA and other corresponding methods generally.
Quantum identity authentication protocol based on three-photon quantum error avoidance code in edge computing

Summary Information security protection is always one of very significant and fundamental issues in edge computing, generally including cryptography, authentication, intrusion detection, privacy protection, and other technologies, and so forth. Quantum authentication is one of the latest extensions of authentication technology in quantum field. So far, most of the quantum identity authentication protocols proposed are based on noise‐free environments. However, due to the existence of quantum noise in quantum channels is inevitable, the noise immunity is very important to quantum identity authentication. In this article, a novel quantum identity authentication protocol based on three‐photon error avoidance code is proposed. In this protocol, quantum information is encoded on the noiseless subsystem, so the protocol can effectively resist on the interference of noise in the quantum channel to information transmission. The comprehensive analysis of antinoise performance and the security analysis on various eavesdropping attacks shows that the new protocol proposed in this article has not only good antinoise performance but also good security.
Abnormal Behavior Detection: A Comparative Study of Machine Learning Algorithms Using Feature Extraction and a Fully Labeled Dataset

Although the number of surveillance cameras in public spaces like streets, banks, parks, shopping malls and others is rising considerably due to low costs of implementation and quick access to technology, the monitoring capability has not increased proportionally. Detecting abnormal behaviors using computer vision and pattern recognition is a long standing challenge. After the research of previous work solutions, we decided to fully label, on a segment level, a dataset with abnormalities, used a generic 3D convolutional neural network to extract feature vectors of each segment and trained a Multilayer Perceptron to do the classification of normal and abnormal behaviors. Our contribution consists, firstly of a fully labeled dataset that is composed of 16853 videos where 9676 videos are labeled as normal and 7177 are labeled as abnormal. Secondly, by the use of the labeled dataset on our proposal, our method outperformed the results of our baseline research with an Area Under the Curve (AUC) of0.863. Finally, we compared our results with other classifiers to demonstrate that the use of a segment-labeled dataset definitely improves the results of the classifiers tested.
Video Target Tracking Based on Adaptive Kalman Filter

Video tracking technology is a hot topic in computer vision research. Video tracking technology is widely used, such as robot vision, intelligent traffic management, medical diagnosis and intelligent monitoring. Therefore, it is of theoretical significance and practical value to study video target tracking technology. In this paper, the background subtraction method and adaptive Kalman filter are combined to realize real time video target tracking. The experimental results show that the proposed method can improve the tracking accuracy.
Identifying Attack Models for Securing Cluster-based Recommendation System

The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, the Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.
Decoupled word embeddings using latent topics

In this paper, we propose decoupled word embeddings (DWE) as a universal word representation that covers multiple senses of words. Toward this goal, our model represents each word as a combination of multiple word vectors that are associated with latent topics. Specifically, we decompose a word vector into multiple word vectors for multiple senses, according to the topic weight obtained from pre-trained topic models. Although this dynamic word representation is simple, the proposed model can leverage both local and global contexts. Through extensive experiments, including qualitative and quantitative analyses, we demonstrate that the proposed model is comparable to or better than state-of-the-art word embedding models. The code is publicly available at https://github.com/righ120/DWE.
Automated Code Transformations: Dealing with the Aftermath

Dealing with legacy systems has been a challenge for the industry for decades. The pressure to efficiently modernise legacy assets to meet new business needs and minimise associated risks is increasing. Automated code transformation, which is associated with serious (long-known) risks, is a high priority in the industrial environment due to the cost structure, the effort required and the supposed time savings. However, little has been published about the long-term effects of successful migrations. This paper looks at three different cases of automated code transformation at different stages of their lifecycle, highlights the lessons learned and derives a number of recommendations that should be useful for planning and executing future transformations.
Healthcare Devices for Children: Strategies to Improve User Experience

Over the time, the concept of caring has undergone many transformations leading to a broader vision of its meaning. In this sense, taking care of oneself and others also implies taking care of the future. There are many aspects connected to the care design from social design to the more specific product design, such as to the design of daily use objects that have the aim of monitoring, improving, facilitating health-related practices and of psycho-physical well-being. The research refers to children as reference public and main users of care devices, because educating them to a good practice from an early age is surely an important aspect to take care of the future.