Reviewerly/exHarmony huggingface dataset:
Cyber-Phishing Website Detection Using Fuzzy Rule Interpolation

This paper introduces a novel detection method for phishing website attacks while avoiding the issues associated with the deficiencies of the knowledge-based representation and the binary decision. The suggested detection method was performed using Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added the benefit of enhancing the robustness of fuzzy systems and effectively reducing the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts in case of phishing attacks. The proposed method was applied to an open-source benchmark phishing website dataset. The results show that the proposed detection method obtained a 97.58% detection rate and effectively reduced the false alerts. Moreover, it effectively smooths the boundary between normal and phishing attack traffic because of its fuzzy nature. It has the ability to generate the required security alert in case of deficiencies in the knowledge-based representation. In addition, the results obtained from the proposed detection method were compared with other literature results. The results showed that the accuracy rate of this work is competitive with other methods. In addition, the proposed detection method can generate the required anti-phishing alerts even if one of the anti-phishing sparse rules does not cover some input parameters (observations).
An Ontology Based Expert System for Lung Cancer : OBESLC

Lung Cancer is the second most recurrent cancer in both men and women and which is the leading cause of cancer death worldwide. The American cancer Society (ACS) in US estimates nearly 228,150 new cases of lung cancer and 142,670 deaths from lung cancer for the year 2019. This paper proposes to build an ontology based expert system to diagnose Lung Cancer Disease and to identify the stage of Lung Cancer. Ontology is defined as a specification of conceptualization and describes knowledge about any domain in the form of concepts and relationships among them. It is a framework for representing shareable and reusable knowledge across a domain. The advantage of using ontology for knowledge representation of a particular domain is they are machine readable. We designed a System named OBESLC (Ontology Based Expert System for Lung Cancer) for lung cancer diagnosis, in that to construct an ontology we make use of Ontology Web Language (OWL) and Resource Description Framework (RDF) .The design of this system depends on knowledge about patient’s symptoms and the state of lung nodules to build knowledge base of Lung Cancer Disease. We verified our ontology OBESLC by querying it using SPARQL query language, a popular query language for extracting required information from Semantic web. We validate our ontology by developing reasoning rules using semantic Web Rule Language (SWRL).To provide the user interface, we implemented our approach in java using Jena API and Eclipse Editor.
Identification of Duplication in Questions Posed on Knowledge Sharing Platform Quora using Machine Learning Techniques

Quora, an online question-answering platform has a lot of duplicate questions i.e. questions that convey the same meaning. Since it is open to all users, anyone can pose a question any number of times this increases the count of duplicate questions. This paper uses a dataset comprising of question pairs (taken from the Quora website) in different columns with an indication of whether the pair of questions are duplicates or not. Traditional comparison methods like Sequence matcher perform a letter by letter comparison without understanding the contextual information, hence they give lower accuracy. Machine learning methods predict the similarity using features extracted from the context. Both the traditional methods as well as the machine learning methods were compared in this study. The features for the machine learning methods are extracted using the Bag of Words models- Count-Vectorizer and TFIDF-Vectorizer. Among the traditional comparison methods, Sequence matcher gave the highest accuracy of 65.29%. Among the machine learning methods XGBoost gave the highest accuracy, 80.89% when Count-Vectorizer is used and 80.12% when TFIDF-Vectorizer is used.
NoSQL Databases

The last chapter described how to create, organize, and query information in SQL databases. We started with SQL because it is a very common standard, and even databases that do not share its relational properties often implement a very similar query interface (e.g., CQL for Cassandra). However, sometimes we would like to make different trade-offs than the ones that relational databases have chosen.
Transformational leadership and team performance: an empirical study of the hotel industry of Pakistan

Pakistan has recently emerged as an important investment destination for the new hotel development. According to Pakistan Hotels Association (PHA), hotel industry has showed a rising trend after the courtesy launch of China Pakistan Economic Corridor (CPEC). The basic aim of this study is to investigate the influence of transformational leadership (TFL) on the team performance of hotel industry of Pakistan through the mediation of team empowerment, knowledge sharing, team-efficacy and team prosocial motivation. This study is based on a sample of 245 employees from different hotels located in Pakistan. Structural equation modelling approach was applied to examine the hypothesised relationships by using smartPLS software. The findings of study revealed the significant positive influence of transformational leadership on knowledge sharing, team empowerment, team-efficacy and team prosocial motivation. Moreover, team empowerment, team prosocial motivation and team-efficacy powerfully mediate the relationship between TFL and team performance. However, knowledge sharing showed limited effect.
Linear and Differential Cryptanalysis

In this chapter, we will discuss two important types of cryptanalysis: linear and differential cryptanalysis. To explain how to merge theoretical concepts with the practical, in the beginning we will go through a set of basic concepts and advanced techniques on how these two types of cryptanalysis can be implemented by professionals.
String Comparison in XSLT with tan:diff()

Classical models of string comparison have been difficult to implement in XSLT, in part because those models are designed for imperative, stateful programming. In this article I introduce tan:diff() , an XSLT function built upon a different approach to string comparison, one more conducive to a declarative, stateless language. tan:diff() is efficient and fast, even on pairs of very long strings (100K to 1M characters), in part because of its staggered-sample approach, in part because of its strategies for optimizing enormous strings (&gt; 1M characters). The output is qualitatively excellent: the function normally returns a minimal diff (shortest edit script) and the longest common substring. Released as part of an open-source function library, tan:diff() enables developers to incorporate robust text comparison directly into XML applications.
Gender Bias in Opinion Aggregation

Gender biases have been documented in areas including hiring, promotion or performance evaluations. Many of these decisions are made by committees. We experimentally investigate whether committee deliberation contributes to gender biases. In our experiments participants perform a real effort task with subjective performance and then rate the task performance of other participants. In a 3 x 2 design we vary the extent to which communication among raters is possible and whether or not the experiment is gender-blind. There is substantial evidence of gender bias with open committee deliberation. In this case 60 percent of ratings received by men are revised upwards after deliberation compared to only 25 percent of ratings received by women. As a consequence women are ranked on average three positions lower after deliberation. We explore several mechanisms and test two interventions for open deliberation. Randomizing the order of speaking does not reduce gender bias, but an information intervention where raters are informed of gender bias in prior sessions does.
Spatial data storage and retrieval in cloud computing environments using attribute based encryption algorithm

Cloud computing consists of the hardware and software systems which is added as services over the network. It offers using resources anywhere and anytime with usage-based pricing, rapid resource elasticity, transference of risk, place unbiased useful resource pooling and so forth. In this paper, we are able to store and retrieve the spatial facts in cloud storage. Security in spatial facts is undertaken by way of the attribute based encryption algorithms which affords efficient encryption of spatial data. In our cryptosystem, put into consequence of multi authority attribute based encryption (MA-ABE) that involves cryptograph texts are categorised based on attributes and personal secrets related to getting admission to systems that manage cipher texts of a consumer is capable to decrypt. This method effectively secures the statistics and additionally gives the correctness of the retrieved facts.
Exploring Mediating Role of Corporate Image between Service Quality and Student Loyalty using PLS-SEM

The demand on high skill graduate shows increasing trend in the context of Malaysia. Current supply of higher skill graduate is in adequate to support the industry. Thus, the government expected that, the number of student enrolment in higher Technical and Vocational Education and Training (TVET) institutions to be increased. Recent studies highlighted that service quality and corporate image are two critical factors that influence student loyalty. However, the mediating effect of corporate image has given less attention in the previous research. Thus, the purpose of this research is to explore the mediating effect of corporate image on the relationship between the service quality and student loyalty. The scope of this study is final year student of higher TVET universities. 398 of completed questionnaires were analyzing using SPSS and Smart-PLS. The measurement model shows that all constructs are reliable. While the structural model result showed that there is a direct and significant effect of corporate image on the relationship between service quality and student loyalty. The mediating test indicates that there is a partial mediation of corporate image on the relationship between service quality and student satisfaction. Future research is suggested to explore in depth on other factors that could have mediate the relationship between service quality and student loyalty.
A framework to improve quality of a Java system by performing refactoring

Software quality is a key pillar that helps provides high reliability for software. Refactoring is one of several methods that helps in improving the quality by mitigating design issues of software without affecting its external behavior. Several automated and/or semi-automated refactoring tools/approaches are available in the literature. However, they lack considering the role of human expertise and/or they may produce side effects that can further negatively affect the quality. Therefore, this paper considers proposing a more mature framework for Java systems that consumes less time and effort. The proposed framework systematically identifies design issues and then prioritise them using the expertise of developers. Further, it applies different refactorings to improve quality. The framework is tested on standard Java software systems and corresponding quality is evaluated for determining its suitability. The obtained results show the effectiveness and practicality of the proposed quality improvement framework.
Counterspeech: A Literature Review

Every day, internet users encounter hateful and dangerous speech online, and some of them choose to respond directly in order to refute or undermine it. We call this counterspeech. Many of those who have taken on this volunteer effort go about it alone, while others organize into groups to coordinate responses and support each other. Some staff at social platforms have touted counterspeech as a method of reducing online hate, but, like Justice Louis Brandeis, who also opined that the remedy for bad speech is good speech, they don’t cite any evidence for their assertions. This is an effort to bridge that gap by answering what should be a prominent question: what does the scholarly literature have to say about the effectiveness of counterspeech?
Opinion Mining using Machine Learning Techniques

Sentiment analysis or opinion mining has gained much attention in recent years.With the constantly evolving social networks and internet marketing sites, reviews and blogs have been obtained among them, they act as an significant source for future analysis and better decision making. These reviews are naturally unstructured and thus require pre processing and further classification to gain the significant information for future use. These reviews and blogs can be of different types such as positive, negative and neutral . Supervised machine learning techniquess help to classify these reviews. In this paper five machine learning algorithms (K-Nearest Neighbors (KNN), Decision Tree, Artificial neural networks (ANNs), Naïve bayes and Support Vector Machine (SVM))are used for classification of sentiments. These algorithms are analyzed usingTwitter dataset. Performance analysis of these algorithms are done by using various performance measures such as Accuracy, precision, recall and F-measure. The evaluation of these techniques on Twitter datasetshowed predictive ability of Machine Learning in opinion mining.
AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

In the big data era, the document classification became an active research area due to the explosive nature in the volumes of data. Document Indexing is one of the important tasks under text classification. The objective of this research is to increase the performance of the document indexing by proposing Adam optimizer in the auto-encoder. Due to the larger dimension and multi-class classification problem, the accuracy of document indexing is reduced. In this paper, an enhanced auto encoder is used based on the objective function of the Adam optimization (AEAO), which improves the learning rate and accuracy of indexing. The documents from the 20-newsgroup data set are converted into vector representation, and then the cosine similarity and Pearson correlation have been measured from the vector. The word to vector representation has words in the vector form and the frequency of words in the document increases their value. The Adam optimization technique selects the features by using similarity values and improves the learning rate. The auto encoder classifier classifies the document based on the objective function of the Adam optimizer. The experiment is conducted using python and the result infers that the classification performance of AEAO is better than that of Similarity-based classification framework for Multiple-Instance Learning and Self-Adaptive LSH encoding for multi-instance Learning techniques in terms of parameters like precision, recall and f-score.
On the Effectiveness of Digital Contact Tracing and Contact Prevention Under Varying COVID-19 Infection Detection Rates

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Malicious user detection with local outlier factor during spectrum sensing in cognitive radio network

In collaborative sensing, multiple secondary users (SUs) cooperate for a more accurate sensing decision to detect spectrum holes in cognitive radio networks (CRNs). This technique, however, can be adversely affected by malicious users (MUs) who route falsified spectrum sensing data to the fusion centre (FC). This attack is known as the spectrum sensing data falsification (SSDF) attack. The task of the FC is to aggregate local sensing reports and is thereby responsible for making the final sensing decision. In this paper, we propose a detection and isolation scheme based on local outlier factor (LOF) to detect and reduce the unfavourable effects of SSDF attack. The key feature of this scheme is that for each SU a metric is calculated, which is called the LOF. Based on the LOF, a decision is made about whether an SU is an attacker or not. We support the validity of the proposed scheme through extensive simulation results.
Hybrid Domain Steganography for Multiple Images using DWT-LSB Method

The technique of hiding information with coexistence of other information is called Steganography. Users usually stores their documents in the form of images so this can be achieved using steganography. Image steganography can be performed in spatial-domain, Transformation-domain and hybrid domain. A new hybrid domain based steganography for hiding multiple image in a single image is proposed in this paper. The properties of Discrete wavelet transforms is used for developing the algorithm that provides security and also reduces the storage capacity in huge databases. While storing various documents of a user in a huge database confidential and integrity has to be Maintained. Possibility of mismatching of document in huge databases is very common and data storage for maintaining those documents are challenging task. In this Approach, multiple secret images are embedded in a single cover image to get stego image using hybrid domain. Discrete Wavelet Transform (DWT) and Least Significant Bit (LSB) techniques are used in embedding multiple images that reduces the storage capacity along with, enough security. The proposed algorithm provides acceptable Peak Signal to Noise Ratio (PSNR) ratio and data capacity of hidden information is more compared to existing methods.
Public Key Encryption for Cloud Storage Attack using Blockchain

Cloud storage enables user to store data and make it available when it is requested by user. Data generated electronically is very important and it must be encrypted to make sure that the data is tramper-proof. There are two important points to be considered, keyword guessing attack and making cloud storage secure from hackers. In Keyword guessing attack the Keywords search by user are encrypted using secure mechanism and securing the cloud storage means use such techniques which assured to give Confidentiality, Integrity and Accessibility using Blockchain Technology. It is decentralized cloud storage which assist different security mechanisms to protect data. Decentralized cloud storage is itself secure than centralized cloud storage. Because the concept of decentralized is not to store data on single storage device but to store on multiple servers. While storing the data on different location it divided into small parts, and at the time of retrieving data it is available as a complete single block of original data. Whereas in centralized cloud storage data is stored on single storage device. As technology progress the risk from fraudulent users also increases. For this reason, we need some encryption, decryption and authentication mechanism to verify user and if it is authenticated allow access to use its data. There are some techniques also available where user made request on cloud server to receive data which makes cloud server to learn keywords except resulting data. In this paper we make an attempt to review encryption and decryption for cloud storage using blockchain technology to improve security of data.
Bit Manipulation

Bit manipulation is an advanced topic that JavaScript developers typically do not need to know. Low-level programming languages such as C take advantage of these operators. However, you should learn a bit about bit manipulation if you want to implement high-performance server-side code.
STATIC EVALUATION OF THE GLOBAL SOLAR POTENTIAL IN THE REGION OF KARA (TOGO) BY EMPIRICAL MODELS

15Oct 2019 STATIC EVALUATION OF THE GLOBAL SOLAR POTENTIAL IN THE REGION OF KARA (TOGO) BY EMPIRICAL MODELS. Serge Dzo Mawuefa Afenyiveh , Koffi Mawugno Kodjo and Hoavo Hova. Laboratory Materials, Renewable Energies and Environment, Department of Physics, University of Kara, Kara, Togo. Research Laboratory in Engineering Sciences, Department of Electrical Engineering, University of Lome, Lome, Togo.
CreativeAI

In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, often beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well, often requiring application-specific learning architectures. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This course gives an organized overview of core theory, practice, and graphics-related applications of deep learning.
Quantum Technologies

Many have heard about quantum computing, but very few understand how the technology works and it is common with misunderstandings. Will it make my computer go faster? Will it change how AI works? Will I soon have a quantum computer in my mobile phone? Is there an app for that? Why is it not here already? Will it ever be? This chapter will discuss some of the core concepts of quantum technologies. We will see that quantum is not only about computing and discuss some possible new applications on the horizon.
A mathematical model for intimacy-based security protection in social network without violation of privacy

Protection against spam, fraud and phishing becomes increasingly important in the applications of social networks. Online social network providers such as Facebook and MySpace collect data from users including their relation and education statuses. While these data are used to provide users with convenient services, improper use of these data such as spam advertisement can be annoying and even harmful. Even worse, if these data are somehow stolen or illegally gathered, the users might be exposed to fraud and phishing. To further protect individual privacy, we employ an intimacy algorithm without the violation of privacy. Also, we explore spammers through detecting unusual intimacy phenomenon. We, therefore, propose a mathematical model for intimacy-based security protection in a social network without the violation of privacy in this paper. Moreover, the feasibility and the effectiveness of our model is testified theoretically and experimentally.
Implementation of QR Code for Sharing Files.

QR code is a quick response code which is used to store information. In QR code, the information is stored in encoded form. To access information present in QR code , we need to decode information with the help of scanner. The information which is present in QR code is accessible to anybody. Private data is not safe in such scenario. This paper presents a visual secret sharing scheme to encode a secret QR code into distinct shares. Visual secret sharing scheme is a method of distributing secrete amongst a group of participants. The secret message is recovered by XOR-ing the shares. Secret message can be generated only when enough number of shares are combined. This provides security for private message using visual secret sharing scheme. Proposed system provides higher security to messages and it also provides more flexible access structure. Computational cost of proposed scheme is low.