Reviewerly/exHarmony huggingface dataset:
An effective movie recommender system enhanced with time series analysis of user rating behavior

Recommender system aims at improvising user satisfaction by taking decision on what movie or item to recommend next. Over time though, learners and learning behaviours shift regularly. This paper introduces a novel behaviour-inspired suggestion algorithm named the TimeFly-PPSE algorithm, which operates on the concept of changing user's motives around time. The suggested model takes temporal knowledge into account and monitors the progression of consumers and items that are useful in providing adequate recommendations. The latter outlines a framework that enrolls the user's shifting behaviour to include guidance for personalisation. TimeFly's findings are contrasted with those of other well-known algorithms. Simulation test on 100K MovieLens dataset shows that utilising TimeFly contributes to recommendations that are exceptionally efficient and reliable.
JAB Code - A Versatile Polychrome 2D Barcode

This paper presents the design and implementation of an encoder and decoder of a colored barcode with high data density and storage capacity and freedom in shape. The approximately three times higher data density compared to conventional 2D matrix codes such as DataMatrix, QR or Aztec code is achieved by the use of eight colors and enables new applications, especially in the endconsumer market as well as in IT security. The challenges associated with the use of the color channel in printing with conventional office printers and recording by smartdevices under typical scenarios are addressed. The flexibility in the barcode shape is achieved by combining a primary and several secondary symbols according to a given scheme and give the necessary freedom for various applications. The presented code stores colors redundantly in a color palette as a reference in order to provide high robustness. JAB code, Just Another Barcode, has been specified, implemented, tested and is avaiable in github and www.jabcode.org under the license LGPL 2.1. JAB code is currently in the standardization process at the International Organization for Standardization ISO.
Classifying Exoplanets with Machine Learning

&amp;lt;div class=&amp;quot;gmail_default&amp;quot;&amp;gt;More than 4200 exoplanets have been detected and their diversity is remarkable, ranging from very small rocky planets, to&amp;lt;br /&amp;gt;puffed gas giants. Several of their types are unknown in our Solar System, hence new classes have been defined to understand this&amp;lt;br /&amp;gt;diversity and the similarities within each group, such as their formation mechanism or core composition.&amp;lt;br /&amp;gt;We aimed to determine the main types of exoplanets, develop a method that automatically associates exoplanets to their type,&amp;lt;br /&amp;gt;classifying them into labels with a machine learning algorithm. We also worked to further understand each group, analysing their&amp;lt;br /&amp;gt;characteristics, and exploring correlations within each group.&amp;lt;br /&amp;gt;Given the planetary mass and orbital period of a large number of exoplanets, we used a K-Means clustering algorithm to classify three large groups: Hot Jupiters, Long Period Giants and Small Planets. In order to take into account more planetary and stellar parameters, we also work with the Uniform Manifold Approximation and Projection (UMAP) technique to visualize&amp;lt;br /&amp;gt;data on a 2D map, aiming to find structures within the high dimensional parameter space. We identified different clusters of&amp;lt;br /&amp;gt;exoplanets on this map with the help of groups already described in the literature. We explored how different sets of input parameters&amp;lt;br /&amp;gt;impact the clustering of exoplanets and studied, in particular, the effect of stellar metallicity.&amp;lt;br /&amp;gt;We were able to identify 5 different groups: Hot Jupiters, Longer Period Giants, sub-Jupiters, sub-Neptunes and Rocky Planets.&amp;lt;br /&amp;gt;We described these groups in terms of values for each parameter, and discussed outliers. We also analysed metallicity separately and verified&amp;lt;br /&amp;gt;that, on average, giant planets orbit around higher metallicity stars than non giant planets.&amp;lt;br /&amp;gt;The well known groups of giant exoplanets, such as Hot Jupiters and Longer Period giants, are clearly identified in the&amp;lt;br /&amp;gt;resulting UMAP 2D parameter space. For smaller planets, several groups were also visible but less separated. We also verified that the global structure is preserved, noticing, for example, the smaller planets (&amp;lt; 8R&amp;lt;sub&amp;gt;&amp;lt;span class=&amp;quot;st&amp;quot;&amp;gt;&amp;amp;#10753;&amp;lt;/span&amp;gt;&amp;lt;/sub&amp;gt;) are grouped together and well separated from the Hot Jupiters. Adding more samples&amp;lt;br /&amp;gt;of well characterized small planets would certainly help their classification.&amp;lt;/div&amp;gt;
Research on the analysis of commercial economic data based on hierarchical clustering algorithm

With the rapid development of China's economy and society, numerical classification has occupied an important position in the economic development. A customer-centric, comprehensive operational service model is the future of the business economy. The core idea is to improve the service quality system and thus the management strategy. The business economy not only grasps the customer's psychological consumption situation, but also manages the market according to the customer's actual demand, through taking the corresponding measures to establish a good customer relationship. Only in this way can the market's operating share be increased, thus achieving the goal of improving the enterprise's economic benefit. Data clustering algorithm refers to a research topic in the research field of data mining. In the research field of data mining, the research work of data clustering is mainly to analyze the data in the database and find the appropriate application mode.
Lighting Technique for Attaching Invisible Information Onto Real Objects Using Temporally and Spatially Color-Intensity Modulated Light

This article describes a lighting technique which enables us to invisibly attach information onto a real object by using illumination light and to extract the information from a video image of the object captured with a video camera. A temporally and spatially color-intensity modulation embeds information into a color component of the light beam. First, we assessed the invisibility to the human eye of the information attached onto a real object visible to the human eye. The experiments showed an upper limit value of modulation amplitude that satisfied invisibility for each color component. The experimental results revealed that the blue color component had a much higher degree of invisibility than the red and green components. We also examined the readability of the information attached to the object with a video camera when the information was embedded in the blue color component of the light beam. The experimental results showed a lower limit value of modulation amplitude that satisfied readability. The experimental results showed the range of modulation amplitude of intensity in the blue color that satisfied the invisibility and readability of the information attached to the object. The results of our experiments demonstrated the effectiveness of the proposed technique.
Medical Data Protection Using Blind Watermarking Technique

With the enormous technological development that the world is witnessing today, almost all living areas have become connected to the digital world. Taking the field of medicine which is considered as a qualitative shift that led to the emergence of telemedicine or e-health. A telemedicine application often relies on cloud computing to store medical’s data. Although this gives a lot of benefits, it also shows real disadvantages that affect the development of e-health systems, including the mediocre degree of protection offers by the public cloud, this has brought the attention of experts and researchers to come up new technologies to address this issue. This chapter presents a new technique for protecting the medical data in an untrusted environment, by using secure blind medical images watermarking technique. The proposed method is based on Number Theoretic Transform (NTT), which guarantees the patient’s information confidentiality with minimum cost in terms of execution time while keeping the medical image quality.
Implementation of a Leganto Reading List service at Curtin University Library

Implementation of Ex Libris Leganto course resource list management software within Curtin University’s learning and teaching environment allowed the Library to improve the management of learning and teaching resources in ways unavailable in the previous reading list system. These included embedding the Leganto software into the University’s learning management system via an LTI (Learning Tools Interoperability) link so that the user interface could be accessed on every user’s desktop computer or device. Fully integrated with the Library’s Alma instance, Leganto enables instructors’ interactions (events/activities) to be directly loaded into the library management system, streamlining technical services work, including reading list creation, acquisitions, copyright management and digitisation. This has led to significant improvements in service delivery and compliance. These advances assure the centrality of the Library to the University’s globalised learning strategy. The flow-on effects have influenced library services and staff roles and provided a foundation for future process enhancements.
A Policy Research on the Developmental Plan of Weeschool: Focusing on the Gyeonggi Saeul School

The purpose of this study was to examine the learning belief factors that foreign students have and their effect on Korean language acquisition.The purpose of this study was to analyze the learning belief factors of foreign students and whether they have the ability to communicate in a second language.Furthermore, it aims to analyze the learning belief factors of foreign students and whether they have the ability to communicate using a second language.In this study, a total of 34 BALLI items were used to examine the learning belief factors and a total of 24 items were used to examine the communication ability factors.The researchers investigated the facilitating beliefs and inhibiting beliefs that foreign students' Korean language learning has on the improvement of second language communication ability, and examine the effect of Korean language learning beliefs on the improvement of second language communication ability.It can be seen that foreign students' beliefs about learning Korean are correlated with learning beliefs and communication skills of foreign language learners in the second language communication ability.This study is intended to contribute to the follow-up research on Korean language education related to the theory of belief in Korean acquisition and the second language communication ability of foreign students.
A Watermarking Algorithm for Cloud Database Based on Chaos Cryptography

In order to solve copyright protection problems of cloud database, watermark can be added with information hiding in network environment. It can take advantage of chaos random public key cryptography to produce different public and private keys, through the controllable factors associated public key to detect the watermark information, which can protect intellectual property rights of cloud database, avoiding leakage of watermark key information.
Zero-cycles on Garbagnati surfaces

Garbagnati has constructed certain surfaces of general type that are bidouble planes as well as double covers of K3 surfaces. In this note, we study the Chow groups (and Chow motive) of these surfaces.
Divide to Better Classify

Medical information is present in various text-based resources such as electronic medical records, biomedical literature, social media, etc. Using all these sources to extract useful information is a real challenge. In this context, the single-label classification of texts is an important task. Recently, in-depth classifiers have shown their ability to achieve very good results. However, their results generally depend on the amount of data used during the training phase. In this article, we propose a new approach to increase text data. We have compared this approach for 5 real data sets with the main approaches in the literature and our proposal outperforms in all configurations.
Train Small, Generate Big: Synthesis of Colorectal Cancer Histology Images

The construction of large tissue images is a challenging task in the field of generative modeling of histopathology images. Such synthetic images can be used for development and evaluation of various types of deep learning methods. However, memory and computational processing requirements limit the sizes of image constructed using neural generative models. To tackle this, we propose a conditional generative adversarial network framework that learns to generate and stitch small patches to construct large tissue image tiles while preserving global morphological characteristics. The key novelty of the proposed scheme is that it can be used to generate tiles larger than those used for training with high fidelity. Our evaluation of the Colorectal Adenocarcinoma Gland (CRAG) dataset shows that the proposed model can generate large tissue tiles that exhibit realistic morphological tissue features including glands appearance, nuclear structure, and stromal architecture. Our experimental results also show that the proposed model can be effectively used for evaluation of image segmentation models as well.
Adaptive Black Widow Optimization Algorithm for Data Clustering

Generally, local search approaches are better than population-based approaches in exploiting the search space and worse for exploration. Recently, the black widow optimisation (BWO) algorithm was proposed for engineering optimisation problems as an algorithm balancing between the exploration and exploitation phases. However, the BWO algorithm employed three essential parameter rates, in which a different experiment is needed for each problem. The adaptive black widow optimisation (ABWO) algorithm is proposed to tune the parameters adaptively and accept worse solutions by relying on a local search, using the solutions' qualities average. Six medical datasets are used as a test domain with two calculations criteria to calculate the minimal distance. In order to evaluate the effectiveness of ABWO, a comparison is made between ABWO, BWO and other methods' performances that have been drawn from the acknowledged literature. Outcomes show ABWO is capable of obtaining better cluster qualities, thus outperforming many other methods.
Hartley Spectral Pooling for Deep Learning

In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-called pooling. Maximation and averaging over sliding windows (max/average pooling), and plain downsampling in the form of strided convolution are popular pooling methods. Since the pooling is a lossy procedure, a motivation of our work is to design a new pooling approach for less lossy in the dimensionality reduction. Inspired by the Fourier spectral pooling(FSP) proposed by Rippel et. al. [1], we present the Hartley transform based spectral pooling method in CNNs. Compared with FSP, the proposed spectral pooling avoids the use of complex arithmetic for frequency representation and reduces the computation. Spectral pooling preserves more structure features for network's discriminability than max and average pooling. We empirically show that Hartley spectral pooling gives rise to the convergence of training CNNs on MNIST and CIFAR-10 datasets.
The comparison between two methods of object detection: Fast Yolo model and Delaunay Triangulation

Image segmentation, object detection and classification are three closely related tasks that can be greatly improved when they are jointly solved by feeding information from one task to another. Different methods have been proposed by the researchers, some of which have given good results and others fail in certain circumstances. In our paper, we compare two techniques for recognizing moving objects in a video scene. The first approach is based on deep learning. We implemented the Fast Yolo model to detect objects. The second approach is based on the segmentation of objects, we used the Delaunay Triangulation method to recover homogeneous regions. We have combined the features of the HOG, color histogram, and GLCM associated with each object. The classification phase is carried out by Alexnet for both approaches. The experiment was carried out on several video clips of highways and local roads with different traffic and lighting conditions.
IRISA System for Entity Detection and Linking at CLEF HIPE 2020

This note describes IRISA's system for the task of named entity processing on historical newspapers in French. Following a standard entity detection and linking pipeline, our system implements three steps to solve the named entity linking task. Named Entity Recognition (NER) is first performed to identify the entity mentions in a document based on a Conditional Random Fields classifier. Candidate entities from Wikidata are then generated for each mention found, using simple search. Finally, every mention is linked to one of its candidate entities in a so-called linking step leveraging various string metrics and the semantic structure of Wikidata to improve on the linking decisions.
Quick and Simple Approach for Detecting Hate Speech in Arabic Tweets

As the use of social media platforms increases extensively to freely communicate and share opinions, hate speech becomes an outstanding problem that requires urgent attention. This paper focuses on the problem of detecting hate speech in Arabic tweets. To tackle the problem efficiently, we adopt a “quick and simple” approach by which we investigate the effectiveness of 15 classical (e.g., SVM) and neural (e.g., CNN) learning models, while exploring two different term representations. Our experiments on 8k labelled dataset show that the best neural learning models outperform the classical ones, while distributed term representation is more effective than statistical bag-of-words representation. Overall, our best classifier (that combines both CNN and RNN in a joint architecture) achieved 0.73 macro-F1 score on the dev set, which significantly outperforms the majority-class baseline that achieves 0.49, proving the effectiveness of our “quick and simple” approach.
Teaching Reform of Object-oriented System Analysis and Design Based on OBE

Combined with the engineering education concept of OBE, this paper aims at the problems of how to strengthen the abilities of analysis and design as well as to improve the learning effects of students who major in software engineering in universities. Focusing on the Learning outcomes students can achieve after completing the learning process, we integrated the OBE concept into the course design and implementation, and optimized the contents of the course, redesigned the assessment from two aspects of teaching content and course examination. We also applied various teaching methods and means to further improve the quality of teaching and talent cultivation. Through teaching reform and practice, the final learning effect of students has been improved.
Blockchain Enabled KYC Solutions using Hyperledge Fabric

Blockchain as one of the most well-known examples of Distributed Ledger Technology, has been hailed across the industry as the key to future success within the financial services industry. Blockchain is a platform that supports multiple use cases within Financial Services, and it has the ability to transform a number of processes, including peer-to-peer payments, trade settlements and supply chain tracking. All these use cases depend on one thing that's the Identification and Verification of participant or users. That is called as Know Your Customer(KYC). Verification of User is one of the basic thing that establishes trust between the participants before making any of the transactions. Ever-increasing anti-money laundering (AML) regulations, coupled with other data protection regulations have led many financial institutions to implement lengthy, expensive processes in order to remain compliant. This paper provides solution to this problem using Hyperledger Fabric, which is framework under blockchain technology, provides some key features like permissioned network, confidentiality of Data, and Programmable. These features establish trust, transparency and accountability.
Design of Cross-border Network Crime Detection System Based on PSE and Big Data Analysis

In order to speed up the detection of cybercrime worldwide, a new cross-border cybercrime detection system is designed by introducing the PSE theory and the principle of big data analysis. In the TFTP server, the U-boot network development board and OpenStack crime information detection component are connected to build the hardware running environment of the cross-border network crime detection system. On this basis, through the analysis of the characteristics of detection information, the calculation of cross-border network detection domain and the directional planning of network crime information, the software operating environment of the detection system is built, and the cross-border network crime detection system based on PSE and big data analysis is designed with the basis of hardware implementation. The comparative experimental results show that, compared with the conventional case detection situation, the average detection time of criminal cases is basically maintained between 3-5 days after the application of cross-border cyber crime detection system, and the accuracy of criminal location is maintained at more than 90%.
Influence Analysis of Smoothing Algorithms in Language Modelling for Indonesian Statistical Machine Translation

The diversity of languages makes the need for translation so that communication between individuals of different languages can be appropriately established. The statistical translator engine (SMT) was a translator engine based on a statistical approach to parallel corpus analysis. One crucial part of SMT was language modeling (LM). LM was the calculation of word probability from a corpus-based on n-grams. There was a smoothing algorithm in LM where this algorithm will bring up the probability of a word whose value was zero. This study compares the use of the best smoothing algorithm from each of the three LM according to the standard Moses, namely KenLM, SRILM, and IRSTLM. For SRILM using smoothing algorithm interpolation with Witten-bell and interpolation with Ristads natural discounting, for KenLM using interpolation with modified Kneser-ney smoothing algorithm, and for IRSTLM using modified Kneser-ney and Witten-bell algorithm which was referenced based on previously researched. This study uses a corpus of 10,000 sentences. Tests carried out by BLEU and testing by Melayu Sambas linguists. Based on the results of BLEU testing and linguist testing, the best smoothing algorithm was chosen, namely modified Kneser-ney in KenLM LM, where the average results of automated testing, for Indonesian-Melayu Sambas and vice versa were 41. 6925% and 46. 66%. Moreover, for testing linguists, the accuracy of the Indonesian-Melayu Sambas language and vice versa was 77. 3165% and 77. 9095%