Reviewerly/exHarmony huggingface dataset:
Proposing a library consortium model for national development in Zimbabwe

Globally library cooperation and collaboration is transforming the scholarship landscape as academic libraries battle to survive amidst escalating costs of subscriptions. Zimbabwe is no exception to this phenomenon as evidenced by the development of local consortia for resource sharing. The main purpose of the research is to gather the views of librarians on the need for a library consortium to support national development in Zimbabwe. The researcher used a mixed method approach. Zimbabwe does not have national library consortium for nationwide access to information. Such a consortium is key to harnessing the collective buying power of member institutions to negotiate and guarantee license agreements for digital content. The study recommends a federated library consortium model built on lessons learned from other countries. The model elevates library consortium principles of cooperation and collaboration into Zimbabwe’s national development agenda.
Towards compact swarm intelligence: a new compact firefly optimisation technique

Firefly Algorithm (FA) is a recent and promising swarm intelligence algorithm. It is inspired by the modelling of brightness and attractiveness manifested by fireflies. Like other population-based algorithms, it presents the drawbacks of high computational cost and memory storage. This paper deals with this problem and introduces a compact firefly optimisation technique with minimal computational and memory requirements. So, we present four new variants of compact firefly algorithm that require only a minimal computational cost. The swarm is compacted and represented by a Probability of Density Function (PDF). This idea is inspired from compact evolutionary algorithms (cEAs). Two solutions of memory storage of the population are presented and analysed. The first is based on normal PDF and the second on uniform PDF. Furthermore, two versions of compact Lévy-flight firefly algorithm (cLFA) are also introduced. This paper takes a step towards new compact swarm intelligence algorithms. The proposed algorithms are compared to the state-of-art of cEAs and two original variants of FA using IEEE CEC2014 functions. In addition, the proposed algorithms are used to realise an optimal swing-up movement of a humanoid robot hanging on a bar.
《千字文》在朝鮮王朝的傳播與影響

Qianziwen was written by Zhou Xingsi. It spread to ancient Korea and had a major influence on Korean literary history. An immediate effect was that many written and printed versions appeared throughout Korea. Some Korean scholars imitated and translated the culturally alien texts of Qianziwen to facilitate the process of Chinese education in Korea. An indirect effect was to encourage the production of books with Korean characteristics. One example of this is the development of dictionary compilations in ancient Korea. This is a special phenomenon in terms of cultural exchanges between China and Korea, and is invaluable for the understanding of the spread of Chinese characters and classics overseas.
SportsStore: Progressive Features and Deployment

In this chapter, I prepare the SportsStore application for deployment by adding progressive features that will allow it to work while offline and show you how to prepare and deploy the application into a Docker container, which can be used on most hosting platforms.
Real Time Cyberbullying Detection

Automated approaches for detecting cyberbullying on online platforms has remained a primary research concern over past years. Cyber bullying is defined as the use of electronic communication to bully a person, typically by sending messages of intimidating or threatening nature. The victims especially teenagers suffer from loss of confidence, depression, sleep disorder. The research on automated cyberbullying approach is mainly focused on data driven methods. Such methods work on a database of static texts, usually collected from online platforms and are not feasible for dynamic nature of a real-life social networking scenarios. The aim of our research is to develop a cyberbullying detection system using Fuzzy Logic. Three types of bullying emotions are considered in this research work namely aggression, abuse and threat. In the proposed approach chat between two users is continuously monitored and emotion present in each message is determined. Based on the emotion each user’s behavior is categorized as decent or bullying. If the detected bullying nature is higher than a defined threshold value the account of user is ceased and reported automatically. The proposed approach is tested with a chat application developed in Microsoft .Net Framework and approach can detect cyber bullying in good time. The proposed approach, if implemented with social networking platforms can serve as a useful aid for preventing online harassment. The developed algorithm can also be applied in surveillance and human behavioral analysis.
Analytical Look - Transparency in Online Informative Technology

We are predicting the bias (left leaning or right leaning) in online news articles based on text of online articles collected and the publication. The rating (either Left or Right) was assigned by looking at the leaning of the publication as found on mediabiasfactcheck.com. As we know the importance of online news has evolved with the advancement in technology. In order to understand the biasness in online journalism related to text of an article, we used a deep Neural Net to make classifications based on the labeling assigned according to publication. If the political bias of the publisher creeps in and such a correlation is there the AI will be able to learn it. Our training produced a very accurate classification model. This shows that online media is not as transparent when presenting news. The methodology is described ahead.
Austermann Receives 2019 Jason Morgan Early Career Award

Jacqueline Austermann received the 2019 Jason Morgan Early Career Award at AGU’s Fall Meeting 2019, held 9–13 December in San Francisco, Calif. The award is “presented annually to an early career scientist for outstanding and significant contributions to tectonophysics through a combination of research, education, and outreach activities.”
MALAWI: Anti‐bribery Protest

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 57, Issue 1 p. 22583A-22583B Internal Developments MALAWI: Anti-bribery Protest First published: 14 February 2020 https://doi.org/10.1111/j.1467-825X.2020.09277.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume57, Issue1February 2020Pages 22583A-22583B RelatedInformation
Secrecy Regulation and Obstruction of Photo Theft on Social Platform

Nowadays, People are well-connected to one another in Social Networks. Usage of Internet and involvement in Online Social Platforms are increasing day by day. People sharing their personal information in these platforms. Even, they started Uploading their Photo in the Internet and it became very common among the Internet users. Without understanding the risk, user involved in sharing their personal details. Some fraudulent users uploading another user’s photo without their knowledge. They are getting photos of others in many ways such as by taking photos using their mobile cameras, collecting images from Social networks, stealing images from the theft mobile. They are posting the photos in the Social media applications which are stored enduringly in the database. This results in the misuse of photo and involved in many criminal activities. To overcome this security problem, a competent face recognition system is needed which identify the user in the photo. To handle this, a set of users, friends list and their private photographs. This system accomplished as an evidence of approach in social network on Facebook application.
Opinion Column: Evolution of topics in the multimedia community

For this edition of the SIGMM Opinion Column, we asked members of the Multimedia community to share their impressions about the shift of scientific topics in the community over the years, namely the evolution of "traditional" and "emerging" Multimedia topics. This subject has emerged in several conversations over the 2 years of history of the SIGMM Opinion Column, and we report here a summary of recent and old discussions, happened over different channels --- our Facebook group, the SIGMM Linkedin group, and in-person conversations between the column editors and MM researchers --- with hopes, fears and opinions around this problem. We want to thank all participants for their precious contribution to these discussions.
Hand Gesture Recognition System Using Deep Learning

Hand motion acknowledgment is a characteristic method for human PC association and a zone of dynamic research in PC vision and AI. This is a zone with a wide range of conceivable applications, giving clients an easier and increasingly normal approach to speak with robots/frameworks interfaces, without the requirement for additional gadgets. Along these lines, the essential objective of signal acknowledgment explore connected to Human-Computer Interaction (HCI) is to make frameworks, which can distinguish explicit human motions and use them to pass on data or controlling gadgets. For that, vision-based hand signal interfaces require quick and incredibly strong hand discovery, and motion acknowledgment continuously. This paper introduces an answer, sufficiently nonexclusive, with the assistance of deep learning, permitting its application in a wide scope of human-PC interfaces, for ongoing motion acknowledgment. Investigations did demonstrated that the framework had the capacity to accomplish a precision of 99.4% as far as hand act acknowledgment and a normal exactness of 93.72% as far as unique signal acknowledgment.
Secure Handshake Hybrid Secured Algorithm for Acceleration-Based Pairing Mechanism in Wrist Worn Devices

With the large spread of wrist worn smart devices in day-to-day lives, an extensive series of applications are enabled. Securing message conversation between these devices has become a challenging issue, considering the high security necessities and low computation abilities of these wrist worn devices. In this paper, we propose a secure wrist worn smart device coupling arrangement by manipulating the wave indication of the strategies generated by the handshake to barter a trustworthy key between users. To guarantee the safety of key negotiation, a hybrid secured algorithm is further developed. On Comparing with existing algorithms, the planned algorithm evades complicated fault alteration algorithms. In existing work the key generation is small in size so it is easy to detect by the intruder, in order to overcome this issue the RSA algorithm is combined with the AES algorithm, So that the size of the key is increased due to the combined algorithm and therefore robustness of the key is increased compared to the existing method. Investigational results are provided, which prove that the proposed handshake acceleration-based coupling arrangement is strong, safe, and effectual.
Design of filter for image de-noising using discrete wavelet transform for ASIP

Application specific instruction-set processors (ASIP) is a customised processor for user specific application. Though a significant research has been done on this, still it is most promising technology, due to lack of efficient methodologies for designing the processor configuration according to the applications. Again ASIP solution explores the trade-off between the dedicated hardware design and flexibility among software. It endeavours to fulfil the functionality of an algorithmic with low power costs and less complexity. In this paper, an approach is considered to design a processor for image de-noising. The design of suitable filter is an important task for the transmission and real-time processing. Designing ASIPs requires a suitable design of custom data path simultaneously modify the instruction-set, decoder including the compiler. We present an ASIP based on custom architecture design using the discrete wavelet transform (DWT) as a filter. It starts with the general purpose data path like MIPS. It customises the data path iteratively for better power utilisation, usable area and performance. All the experiments have been synthesised using Xilinx FPGA and also verified in Spartan board. The subjective evaluations of the filter is analysed through various figures. Further it is implemented in HDL to support the customised processor.
High-dimensional Arnold inverse transformation for multiple images scrambling

The traditional scrambling technology based on the low-dimensional Arnold transformation (AT) is not able to assure the security of images during the transmission process, since the key space of the low-dimensional AT is small and the scrambling period is short. Actually, the Arnold inverse transformation (AIT) is also a good image scrambling technique. The high-dimension AIT used in the image scrambling, can solve the shortcomings of low-dimensional geometric transformation, have good image scrambling effect, and achieve the purpose of image encryption, which enriches the theory and application of image scrambling. Taking account that an image has location space and colour space, the high-dimensional AIT for image scrambling improves the anti-attack ability of image scrambling since the combination of the location space coordinates and the colour space component is very flexible. We investigated the property and application of AIT with five- or six-dimension in the digital images scrambling. Specifically, we proposed the theory of n-dimensional AIT. Our investigations show that the technology in larger key space has good effects on scrambling and has a certain application value.
A Novel on Classification Techniques of News with Help of Sentiment Detection

The problem of data classification is an important topic in the field of machine learning and information retrieval. This has been widely studied and has been applied in many fields. There are multiple models which are proposed for the classification, like tree-structured classifiers, genetic algorithms, Bayesian classification, neural networks etc. These have a large range of applications in different areas like, fraud/spam detection, Customer Segmentation, Medical Diagnosis, Credit approval, weather prediction etc. This project tries to aim at a particular subclass of classification, namely sentiment analysis. Hybrid techniques should be applied in this field of study as each of the existing models have brought about some new expertise and their improvements need to be combined to give higher performance and accuracy. The sentiment analysis problem requires to take as input a block of text and correctly predict the sentiment of the writer or the speaker of the text. We have sufficient data to build a system that uses hybrid techniques like Naïve Bayes and combines the existing models to perform sentiment analysis on a dataset and study its results. The hybrid approach using Naïve Bayes to this problem gives promising results.
Emerging Microcomputer Technology: Impact on Bibliographic Instruction

The basic purpose of library use instruction is to enable the library users, typically college and high school students, to learn how to access the resources that are available in the library. The current generation of bibliographic instruction programs should take into consideration the available microcomputer technology and fully exploit it—keeping in mind the basic objective of the library instruction program. The currently available technology might resolve that conflict in favor of the students. The curriculum of early colleges emphasized Greek and Latin precisely because studying Greek and Latin was supposed to strengthen mental and moral disciplines — many librarians still harbor such views. Downloading is the next logical step from online databases and database management programs. Everybody knows that word processors come into play and that they are a hundred-fold improvement over typewriters.
Smart Resilient Security Framework and Solutions for Cloud-driven Digital Supply Networks

The cloud-based logistics services allow cost reduction, higher elasticity, flexibility, and maximum utilization of resources for performing high computations and data analytics. Logistics and security are complex issues with the corporate entities when these are to be used for taking critical issues. To understand these, this paper discusses a conceptualized, flexible security framework for cloud-driven digital supply chain in Agricultural. Moreover, a security enhancement layer is included at each layer of the cloud with a feasibility study on protecting user information in the logistics services ambiance. Also, the Data Residency for cloud-based logistics services is elaborated with Data Security analysis. Further, the article discusses the possible solutions to handle the security concerns of the logistic model.
A Method of Scene Boundary Detection for Indexing Video Data Efficiently

In a cell animation, a background scene is presented with one cell. When animation scenes are changed, a relatively large scene change occurs because their backgrounds are also changed. Unlike a camera-based real movie, a cell animation is made in the way of manual drawing. For this reason, such animation does not use many colors. In order to apply the characteristics of animations as most as possible and detect a scene change of a cell animation effectively, this study proposes a new scene change detection technique with the stepwise use of the color and block-based histograms. The proposed algorithm receives continuous animation images as input, changes the RGB color space into the HSI color space, executes the difference operation of color values of the two images, and thereby primarily determines whether the neighboring images are scene change candidates. If they are judged to be scene change candidates, the color histogram for each sub-region is made, and then a weight value is applied to finally determine whether a scene change occurs. To compare the proposed scene change detection method with conventional scene change detection methods, the block-based scene change detection method and the histogram-based scene change detection method as conventional ones were implemented. In this study, to qualitatively evaluate the performance of the proposed method of detecting scene boundaries, the accuracy measure was defined. The two conventional algorithms are not complicated relatively and are widely used to extract a scene change. The experiment of this study reveals that the proposed method more accurately detects an animation scene change than other conventional methods. The proposed animation scene boundary detection method is expected to be applied usefully to the fields of digital video data indexing and retrieval, dynamic motion analysis, and other related areas.
Simulasi Perbandingan Filter Savitzky Golay dan Filter Low Pass Butterworth pada Orde Ketiga Sebagai Pembatal Kebisingan

Nowadays, the Information and Communication Technology (ICT) is rapidly developed. It also trigs the development of other research field such as social science research. But in the development of it, there are a continues problem that has been discovered over 30 years, noise. Over the years, many ways have been created for example Savitzky – Golay (SG) and Low Pass (LP) Butterworth filter. In order to use SG filter, two parameters which are the order and the window length should be determined by trial and error. On the other hand, LP Butterworth filter also needs two parameters to be operated which are the order and the cut off frequency. This research focuses on comparing the performance of third order LP Butterworth filter and third order SG filter by finding the gap between filtered signal and the original signal through the simulation by using MATLAB. This research is supported by the journals and books references. Also, the data of this research is presented by the table and graph. According to this research, founded that both filters have a significant impact to smoothing the noisy signal. compare to LP Butterworth filter, SG filter has better performance. It is proven by SG filter only has 5% gap to the original signal where LP Butterworth filter has a slightly bigger gap, 8.82%.
3DMSNET: 3D CNN Based Brain MRI Segmentation

Segmentation of the brain images has become an important task to analyze the abnormality in infants. Automatic methods are important as the infant brain growth has to be tracked and it is almost impossible for an individual to manually segment the MRI data on particular intervals. The manual segmentation tasks are time-consuming and require highly skilled professionals to segment images. Automatic segmentation methods have gained huge support for segmenting MRI images. Several segmentation methods lack accuracies due to nearest neighbor or self-similarity problems. The CNNs have outperformed the traditional methods and are proving to be more reliable day by day. The proposed method is a patch-based method which uses 3DMSnet (3DMulti-Scale Network) for segmentation. The model is evaluated on BrainWeb and other publicly available datasets.
A phase entropy based novel machine learning structure conditioned for classifying ictal and non-ictal signal aimed at proper clinical diagnosis

Proposed scheme is based on the detection and classification of ictal and pre-ictal electro-encephalogram (EEG) signal of a partially affected epileptic patients. This work helps for the detection of ictal EEG signal as compared to pre-ictal EEG signal. In this work a conceptual method is used for the perfect detection of seizures. Here phase entropies (PEnS1 and PEnS2) are followed for feature extraction ictal and pre-ictal EEG signals. Again, extracted features are classified through multi-layer perception neural network (MLPNN) classifier tool. To train and test the classifier, map reduce quantum PSO (MRQPSO) is used. By considering various statistical parameters like Accuracy, Sensitivity, Specificity, positive predictive value, negative predictive value and Matthew's correlation coefficient, the performance of the proposed scheme has evaluated.
Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning

As per Reserve Bank of India (RBI) the number of credit card users is increasing rapidly. Due to the popularity of online banking, there is demand for more fraud detection techniques to protect or prevent cardholders from losses. The proposed method has been implemented on three publicly available data sets. To determine the fraud transaction various deep learning architecture like Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) have been used. Various parameters of deep learning like activation function, number of hidden layers, etc. have been used and it has tune to examine the performance of the model. All the collected data sets were imbalanced therefore, synthetic minority over-sampling technique (SMOTE) has been used. For the evaluation of the model F1 score has been considered as the main evaluation metrics. After the implementation it was discover that in all the three data sets the highest F1 score is achieved by the GRU architecture using sigmoid activation function, and two hidden layers when it compares with the other architecture.