Reviewerly/exHarmony huggingface dataset:
An ontology-driven perspective on the emotional human reactions to social events

Social media has become a fulcrum for sharing information on everyday-life events: people, companies, and organisations express opinions about new products, political and social situations, football matches, and concerts. The recognition of feelings and reactions to events from social networks requires dealing with great amounts of data streams, especially for tweets, to investigate the main sentiments and opinions that justify some reactions. This paper presents an emotion-based classification model to extract feelings from tweets related to an event or a trend, described by a hashtag, and build an emotional concept ontology to study human reactions to events in a context. From the tweet analysis, terms expressing a feeling are selected to build a topological space of emotion-based concepts. The extracted concepts serve to train a multi-class SVM classifier that is used to perform soft classification aimed at identifying the emotional reactions towards events. Then, an ontology allows arranging classification results, enriched with additional DBpedia concepts. SPARQL queries on the final knowledge base provide specific insights to explain people's reactions towards events. Practical case studies and test results demonstrate the applicability and potential of the approach.
Maximal and closed frequent itemsets mining from uncertain database and data stream

Frequent itemsets (FIs) mining from uncertain database is a very popular research area nowadays. Many algorithms have been proposed to mine FI from uncertain database. But in typical FI mining process, all the FIs have to be mined individually, which needs a huge memory. Four trees are proposed in this paper which are: (i) maximal frequent itemset from uncertain database (MFU) tree which contains only the maximal frequent itemsets generated from uncertain database, (ii) closed frequent itemset from uncertain database (CFU) tree which contains only closed frequent itemsets generated from uncertain database, (iii) maximal frequent itemset from uncertain data stream (MFUS) tree which contains maximal frequent itemsets generated from uncertain data stream and (iv) closed frequent itemset from uncertain data stream (CFUS) tree which contains closed frequent itemsets generated from uncertain data stream. Experimental results are also presented which show that maximal and closed frequent itemsets mining requires less time and memory than typical frequent itemsets mining.
Text Generation using Neural Models

The use of automatically generated summaries for long/short texts is commonly used in digital services. In this Paper, a successful approach at text generation using generative adversarial networks (GAN) has been studied. In this paper, we have studied various neural models for text generation. Our main focus was on generating text using Recurrent Neural Network (RNN) and its variants and analyze its result. We have generated and translated text varying number of epochs and temperature to improve the confidence of the model as well as by varying the size of input file. We were amazed to see how the Long-Short Term Memory (LSTM) model responded to these varying parameters. The performance of LSTMs was better when the appropriate size of dataset was given to the model for training. The resulting model is tested on different datasets originating of varying sizes. The evaluations show that the output generated by the model do not correlate with the corresponding datasets which means that the generated output is different from the dataset.
AN INVESTIGATION INTO ISSUES IMPEDING HIGHER EDUCATION EFFECTIVENESS IN PUBLIC UNIVERSITIES

The effectiveness of public universities in Nigeria has continued to generate discussion and is a matter of urgent attention in achieving the mission and vision of the higher education system.This study was carried out to investigate the issues obstructing the effectiveness of public universities in Nigeria.The conceptual framework was adapted from the European Foundation for Quality Management Excellence model (EFQM).This study adopted a generic qualitative design to collect data through semi-structured interviews from eight participants, including the deputy vice-chancellor, deans, head of departments, and lecturers that were purposively selected.Data collected were analysed using thematic analysis with Nvivo (version 10).The findings of the study revealed that leadership, academic and administrative issues, contextual issues and funding are the major factors affecting the effectiveness of public universities in Nigeria.Therefore, for a university in Nigeria to be viewed as a hub of developing nations that strive to meet the expectation of the society, the identified hindrances must be thoroughly interrogated and resolved.
Fintech, Digital Payments, and the Risks of Outsourcing Payroll Accounting: The Case of MyPayRollHR

Payroll accounting requires specialized knowledge and expertise that many small businesses lack. Employee recruitment, retention and satisfaction are highly dependent on the employer’s ability to efficiently process payroll in a timely and dependable fashion. Firms outsource payroll to devote more time and resources on their primary business and strategic goals which may have a bigger impact than processing payroll does, on the firm’s triple bottom line – people, profit, and the planet. While the decision to outsource payroll can help ensure employees are paid and taxes are withheld in a timely fashion, there are inherent risks in handing off this important function to a third-party processor. The ultimate responsibility for accurate and timely processing of payroll remains with the employer. So, firms seeking to outsource payroll need to perform adequate due diligence to ensure there are proper financial controls at third party payroll processors to prevent fraud and misappropriation. This paper presents a recent real life case study that explains the potential pitfalls of outsourcing the payroll function especially when digital payments are used.
Efficient and secure data sharing with outsourced decryption and efficient revocation for cloud storage systems

Data access control is one of the major issues in cloud storage systems, especially when having multiple co-existing authorities requiring a multi-authority access control scheme. Existing multi-authority systems were able to achieve the required level of security and fine-grained access control, but since the cloud services can be accessed using devices with various computation capabilities, it is highly required to have a scheme with efficient encryption and decryption operations. In this paper, we propose a scheme able to achieve the desired level of security and fine-grained access control while having an efficient revocation operation. Moreover, we were able to increase the efficiency of the decryption operation by outsourcing most of the computation to a proxy-server without compromising the confidentiality of the data. In addition, we propose the usage of parallel processing in order to increase the efficiency of the various operations to efficiently use the computational power in low-end devices and the proxy-server. The analysis and simulation results show that ESS-ODER is efficient and provably secure.
Imputing the Missing Values in IoT using FRBIM

The Internet of Things (IoT) is the new-fangled communication paradigm in which the internet is stretched out from the virtual world to intermingle with the objects in the physical world. It unleashes a new dimension of services but at the same time, colossal challenges have to be conquered to reap the full benefits of the IoT. One such challenge is missing data imputation in Internet of Things. The presence of missing values hampers the subsequent processes such as prediction, control, decision making etc. due to the dependency of these processes on complete information. In this paper, a novel FRBIM (Fuzzy Rule-Based Imputation Model) model is proposed to impute missing data based on the characteristics of IoT data to accomplish high accuracy rate. Experimental results have proved that the proposed method has outperformed the existing KNN and AKE imputation model in terms of accuracy.
Parameters optimisation of support vector machine using modified grasshopper optimisation algorithm-based Lèvy-flight method

Grasshopper optimisation algorithm (GOA) is one of the most recent meta-heuristic optimisation algorithms. It was first developed by Saremi et al. in 2017. Although, GOA has shown good performance, it still has demerits with respect to low precision, slow convergence and easily stuck at local minima. This paper presents a modified version of GOA based on Lèvy-flight method, called as (LevyGOA). The proposed LevyGOA is proved to provide a better trade-off between exploitation and exploration, which makes LevyGOA faster and more robust than GOA. LevyGOA is further compared with other meta-heuristic optimisation algorithms and the basic GOA for solving two optimisation problems. These problems are global optimisation problem and parameters optimisation of SVM, where CEC 2005 and CEC 2017 global benchmark functions and six well-known benchmark datasets are used. The experimental results show that LevyGOA can significantly improve the performance of GOA. The results demonstrate that LevyGOA outperforms the other algorithms on a majority of the benchmark functions and benchmark datasets.
A novel hybrid image hiding technique using elliptic curve cryptography and DNA computing technique

The proposed novel hybrid image hiding techniques uses the security features of ECC for encrypting the secret image before embedding with cover image and also map the secret image with DNA nucleotide to improve the level of security with high embedding capacity. In the proposed image steganography, the secret image is fed as input to the ECC encryption algorithm and the resultant ciphertext point is mapped with DNA nucleotide, and then converts it into binary digits. Second, the cover image is compressed by using DWT compression techniques. The compressed image is used as a cover image to embed the secret image by using a singular value decomposition algorithm. The resultant stego image will have high PSNR and less MSE. Results show that 36.23% decreased value of MSE value and 5.6% increased value of PSNR for 512 × 512 image size.
Adaptive Changes in the Psyche of Homo sapiens during the Period of the Singularity (Part 2)

Creativity, the integrative function of the psyche of Homo sapiens (HS), which arose about 50,000 years ago, allowed HS to project the image of the goal (IG), transformed into a “symbol,” into the external environment. The projection constructs of the psyche have become autonomous fragments of the environment in the HS perception, not being its derivatives. Objective reality, perceived by HS, has acquired non-inherent properties: a mental product of the psyche that integrates the virtual and real components of the environment. In other words, the role of HS has evolved over the last 10,000 years, since the beginning of the agrarian revolution: from (1) a dependent subject controlled by external forces in an animated world to (2) “the crown of God's creation” in “theism” and, (3) to the status "Higher power" during the period, in which "God is Dead." Initially, HS exists in an incompletely real environment, with an increasing component of virtuality. With symbolic virtual content, HS supplemented or duplicated the entire surrounding world, creating a two-component habitat (virtual and real). The emergence and development of conceptual thinking (ConceptT) led to a partial "devirtualization" of the environment, the removal of restrictions on scientific knowledge, the rapid growth of technology and social dynamics. The result of technological development was, in the recent past, a temporary resolution of the primary frustration: the establishment of the current equilibrium in the relationships with the regulatory "dissociated symbol"—the virtual "information universe” (IU). The IU, defining as the interference of "media" with the "information body" of the Internet, we consider as a unified information space, integrated with reality and in total constituting the HS habitat. Clip thinking (ClipT), qualitatively different from ConceptT, is a new operating system of the psyche, a moderator of adaptation to a new, virtualized environment. A technological derivative of mental activity HS—the IU—without the participation of conscious forms of mental activity, transforms the algorithms of thinking, i.e. formats the psyche as a whole with adaptation to qualitative and quantitative changes in the virtual component of the environment, and to the perspectives on the development of technologies during the singularity.
Education and training

Certain skills and qualifications, such as those related to science, engineering and ICTs are especially useful for thriving in the context of the digital transformation. In 2016, out of all tertiary graduates in OECD countries, 6% did so with degrees in natural sciences, mathematics and statistics; 14% in engineering, manufacturing and construction; and nearly 4% in ICT fields. It should be noted, though, that modern degree programmes in other fields can also endow students with relevant ICT skills. For example, graduates in the arts, graphic design, journalism and information, 6% of all tertiary graduates in OECD countries, are increasingly involved in activities related to production and management of digital content.
A Fully Automated Solar Tracking System

This is dual axis tracking system, the path of the sun is been tracked by the system is been determined in this procedure. The optimization goal is to increase the amount of generation energy with the help photovoltaic system considering the tracking system consumption. Determination of the tilt angle and azimuth angle trajectories is described as a nonlinear and bounded optimization problem, in future there will be drastic amount of shortage of non-renewable energy resources, we have to replace these resources ,For collection of solar energy we need to improve of efficiency we used sensors (LDR) for tracking of suns path to make sure that the panel should be placed in MPPT-(maximum power point tracking) to observe more efficiency to make sure that the panel should be placed in MPPT point.
ANALYSIS OF THE USE OF VIRTUAL REALITY TECHNOLOGY IN GAMIFIED LEARNING

Virtual reality (VR) which is the world's state-of-the-art learning technology, is an innovative way of teaching students anything and increasing their attention.Students enter to an educational adventurous world through special headphones and devices.VR labs that contain this technology, expand the borders of schools, affordably.Cost of research centers such as science laboratories which are today, considered as luxury, decreases by means of VR labs and VR technologies inside those labs.Since VR technology is based on computers which are already present at schools, it is possible to increase students' success on a large scale for every school.The most important gains of VR labs are savings of time and space.Students who use VR as an educational tool, explore unreluctantly, adjust to studies easily and subjects of education quickly.VR systems which provides a real time virtual learning environment, offers simulation scenarios to improve students' problem solving and analytical skills.By means of these systems students will be able to solve problems through researcher and cooperative experimental activities.Scenarios are being developed in accordance with current math and science classes by using games and digital modelling.Students can use these systems to quickly interact with anyone and share their ideas and experiences.VR systems can also help students develop their language skill by encouraging them to participate in discussions, speeches open to general public, live audio communications and broadcastings.For students to receive feedback and have more time will have a positive contribution to their studies.Starting from this point of view VR technology is at the center of the study.By using gamified learning analyze through VR technology, some important data will be presented about the development of VR technology as a new media application of the future.
VIKAS: a new virtual keyboard-based simple and efficient text CAPTCHA verification scheme

Nowadays online transactions are becoming ubiquitous that must be protected from bots using different techniques, and CAPTCHA is one of them. Text-CAPTCHA preferred due to its simplicity amongst different types of CAPTCHAs. Text-CAPTCHA can be strengthened by adding some distortion to prevent bot-attacks but cause usability issues for humans. This results in multiple attempts by a user to gain access to the required service and may give frustration to the user. Hence, there is a need to design CAPTCHA which is easy for humans to recognise but difficult for bots. This paper proposes virtual keyboard-based simple and efficient text-CAPTCHA verification scheme (VIKAS) that makes CAPTCHA verification easy for humans but difficult for bots. VIKAS uses simple text-CAPTCHA and verifies the same using positions of the keys pressed by the user using an image-based virtual keyboard. VIKAS is sustainable against segmentation scheme, replay attacks and possible attacks with keyloggers.
SEMANTIC INTEGRATION OF MULTIPLE HEALTH DATA FOR TREATMENT DECISION-MAKING IN LOW-RESOURCE SETTINGS

Accurate treatment decision-making for disease treatment is important, particularly if it is done in a way that helps to overcome the challenges associated with low resource setting.This include shortage of qualified personnel, infrastructure, ready access to devices, and healthcare by common people.Although real-time gait analysis has been used for the diagnosis of diseases that are associated with gait impairments, the need to improve on the performance of gait-based prediction by augmenting it with other sources of knowledge in a complementary way has been acknowledged.In contrast, to existing approaches, this paper presents the design of a conceptual framework that will enable the semantic integration and use of information from multiple health data sources in order to ensure efficient treatment decision making for gait-related diseases in a low resource setting such as South Africa.The analytical evaluation of the proposed framework suggests that it has sufficient merit and potential to be usable and useful in low-resource settings.
Clustering of the Multi-Value Documents based on Probabilistic Features Association Mechanism

It is becoming increasingly difficult to cluster multi-valued data in data mining because of the multiple data interval values of individual functions. Identifying a clustering model that is appropriate for these disguised multi-valued data deployments in data analysis applications is an open problem. To answer this question, this paper proposes a feature selection based on the probabilistic features association mechanism (PFAM). The problem is mainly due to the difficulty in identifying the class information and the multiple values for each individual features. This work explores the problem of unsupervised feature selection through computing the probabilistic association score and multi-value data reformation for effective clustering in multivariate datasets. By minimizing a reformation clustering error, it can conserve together the degree of similarity and the categorization information of the actual data contents. The proposed approach is evaluated the clustering purity and Normalized Mutual Information on multivariate document datasets. The experimental evaluation shows the improvisation of the proposed approach.
Development of English-Hindi Interactive Machine Translation

Machine Translation systems are still far from being perfect and to improve their performance the concept of Interactive Machine Translation (IMT) was introduced. This paper proposes an IMT system, which uses Statistical Machine Translation and a bilingual corpus on which several algorithms (Word error rate, Position Independent Error Rate, Translation Error Rate, n-grams) are implemented to translate text from English to Indian languages. The proposed system improves both the speed and productivity of the human translators as found through experiments.
SISTEM CERDAS PENGELOMPOKAN MAHASISWA BERDASARKAN PREDIKSI PERFORMA BELAJAR DENGAN METODE CASE BASED REASONING

To make students can learn well, it is necessary to group students according to their abilities. This grouping of students aims to allow students to share knowledge based on the diversity of learning performance of each student. Students' academic performance can be influenced by several factors including intellectual, ability to learn and personality of each student. From these factors obtained academic performance results are grouped into five pieces of learning performance that will later be used as a rule base or rule base in the system. Rule base that is used as a benchmark for academic performance of students is (1) very bad, (2) bad, (3) medium, (4) good, and (5) very good. Grouping students based on predicted learning performance using case based reasoning method with nearest neighbor algorithm. The accuracy of the results of learning performance recommendations using Confusion Matrix is 91.82%
Fuzzy Sets

In this chapter, we are going to cover the fuzzy sets in several forms and discuss the properties and any other related preliminaries.
Arthrex, Inc. v. Smith &amp; Nephew, Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-2140 (Oct. 31, 2019)

Biotechnology Law ReportVol. 38, No. 6 Case in PointArthrex, Inc. v. Smith & Nephew, Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-2140 (Oct. 31, 2019)Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:9 Dec 2019https://doi.org/10.1089/blr.2019.29148.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 6Dec 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Arthrex, Inc. v. Smith & Nephew, Inc. United States Court of Appeals of the Federal Circuit, 2019 No. 2018-2140 (Oct. 31, 2019).Biotechnology Law Report.Dec 2019.395-402.http://doi.org/10.1089/blr.2019.29148.cmhPublished in Volume: 38 Issue 6: December 9, 2019PDF download
Application of Computer and Mathematics Software in University Mathematics Teaching

In recent years, the development of science and technology is becoming more and more rapid, computer technology is constantly improved, and the fields involved are also increasing. The application in the field of education is also further expanding, which plays a crucial role in college mathematics. The world’s research on mathematical software originated in the 1950s, and began to occupy a very important position throughout mathematics. This paper is mainly aimed at the application of computer and mathematics software in college mathematics teaching, to provide new ideas for the development of university mathematics, heap computer technology and mathematics software for a series of optimization and improvement, give play to students' independent ability, improve the teaching methods, so that the university of mathematics teaching has been steadily improved.