Reviewerly/exHarmony huggingface dataset:
Fault-based testing for discovering SQL injection vulnerabilities in web applications

In this paper we proposed a model to investigate the behaviour of websites when dealing with invalid inputs. Many vulnerabilities rise from invalid inputs. An invalid input is considered as a form of a successful attack if it is processed by the website code or back-end database. Based on this assumption, we proposed a list of indicators that tested and processed invalid inputs. A tool is developed to implement this model. We tested the model through evaluating several websites selected randomly. Our tool has no special credentials or access to any of the tested websites. We found many SQL injection vulnerabilities based on our proposed model. Upon the manual investigation of the web pages that showed such vulnerabilities, we found few instances of false positives. We believe that this can provide a systematic and automated approach to test websites for vulnerabilities related to improper input validation.
Text Mining for Multiclass Research Paper Categorization

A research paper is a rich source of academic and innovative writing on a particular topic, and they are unstructured in nature. Categorization of documents refers to classification of documents in classes that are predefined. It is arduous for a user to categories research paper in different domains: because extracting meaningful and relevant words from the research paper is a challenging task. For extracting important information we have used certain methods and classifiers. Methods like bag of words and tfidf is used for processing data. Prepossessing the data includes string tokenizing and stop-word removal. Then the processed data is classified using SVM classifier. For multiclass classification; since predefined classes are 4, therefore 1-v-r classifier is used. The system performance is 88% with 800 training and 200 testing documents. It is analyzed that the model performs better when the training data is more. The aim of this work is to categorize the documents and allocate set of predefined tag to them. It also evaluates the performance of the model by considering different percentages for training and testing sets of documents.
A new efficient privacy-preserving data publish-subscribe scheme

Data publish-subscribe is an efficient service for users to share and receive data selectively. Due to the powerful computing resources and storage capacity, the cloud platform is considered as the most appropriate choice to publish and subscribe large-scale data generated in real-world life. However, the cloud platform may be curious about the content of published data and subscribers' interests. In this paper, we aimed at realising a secure and efficient privacy-preserving data publish-subscribe scheme on cloud platforms. On one hand, we adopt ciphertext-policy attribute-based encryption (CPABE) to encrypt the data based on it's access policy. Moreover, part of the decryption computation is shifted to the cloud platform to reduce subscribers' computation overhead. On the other hand, we utilise an efficient searchable encryption scheme based on Bloom Filter tree (BFtree) to protect subscribers' privacy and match their interests with encrypted data. Not only that, publishers and subscribers can also exchange their roles in our scheme. The security analysis and experimental results prove that our scheme is efficient and secure in privacy-preserving data publish-subscribe service.
Glomfjord Hydrogen plans large-scale green hydrogen production

Norwegian company Glomfjord Hydrogen has signed a Letter of Intent with an international partner, currently unnamed, to develop large-scale ‘green' hydrogen production and liquefaction in Glomfjord, on the northwestern coast of Norway.
Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive sum-mary by focusing on a unified presentation model with aspect based Information Extrac-tion (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence genera-tion makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.
The impact of effective organisational communications on financial performance

Since effective communications can create a sense of trust between employees and increase customers' satisfaction, the purpose of this research is to investigate the effects of organisational communications on financial performance. Since this study seeks to determine the type and extent of correlation between variables, the descriptive-correlative method is used in this study. This study is an applied research in terms of its goals and is a cross-sectional research in terms of time. The statistical society of this research includes all employees in the finance department of Jahrom University of Medical Sciences and its colleges, which consists of 51 employees. A total of 45 questionnaires were collected. The questionnaire used in this study was composed of two financial organisational questionnaires (21 items) and Robbins effective organisational communication questionnaire (24 items). Study findings showed that there is a positive relationship between organisational communication and financial performance in this university.
Conflation Methods in Stemming Algorithm

We began the domain examination process by social occasion source information. We gathered distributed papers in the conflation algorithms branch of knowledge as domain archives and the source code of conflation algorithms for system engineering examination. In the wake of building skill about the conflation algorithms domain, we rounded out system portrayal surveys for every last one of these algorithms. Imperative segments of our domain investigation process are in the accompanying subsections. With the gigantic measure of information accessible on the web, it is extremely fundamental to recover precise information for some client inquiry. There are heaps of methodologies used to expand the adequacy of online information retrieval. The conventional methodology used to recover information for some client question is to search the reports present in the corpus word by word for the given inquiry. This methodology is extremely tedious and it might miss a portion of the related records of equivalent significance. Therefore to stay away from these circumstances, Stemming has been broadly utilized in different Information Retrieval Systems to build the retrieval exactness
MQDSS

In this chapter we introduce the MQDSS signature scheme, which is one of the few provably secure multivariate public key cryptosystems. We start by a description of the MQ based identification scheme which allows a prover to identify himself using a zero knowledge proof based on the knowledge of the solution of a random system. We then describe the Fiat-Shamir construction of transforming an identification to a signature scheme and finally present the MQDSS signature scheme.
The Power of Deep Learning Models: Applications

The extraordinary research in the field of unsupervised machine learning has made the non-technical media to expect to see Robot Lords overthrowing humans in near future. Whatever might be the media exaggeration, but the results of recent advances in the research of Deep Learning applications are so beautiful that it has become very difficult to differentiate between the man-made content and computer-made content. This paper tries to establish a ground for new researchers with different real-time applications of Deep Learning. This paper is not a complete study of all applications of Deep Learning, rather it focuses on some of the highly researched themes and popular applications in domains such Image Processing, Sound/Speech Processing, and Video Processing.
Marine biogeochemistry

This manuscript presents the results of a study in which experiments on impacts of warming and prolonged darkness on CO2 and CH4 fluxes are conducted in seagrass ecosystems of the Red Sea.Results show upward shifts in carbon dioxide and methane fluxes with warming and in the dark with a few exceptions under varied experimental conditions.Though it is known that a rise in temperature would increase metabolic rates the present set of results confirm thus driven elevated CO2 and CH4 fluxes for seagrass meadows in the Red sea.These results are of significance to understanding and quantifying the forcings and feedbacks of climate system.The Results and Discussion Sections were presented well but I found it difficult to follow some statements in Introduction section.Besides there is need to improve clarity to Material and
A structural taxonomy for metaheuristic optimisation search methods

Metaheuristic search algorithms have become ubiquitous in the applied optimisation world. Various works have appeared classifying and improving these algorithms and the particular processes embedded within the algorithms. Successful metaheuristic approaches have a common general structure to their search processes. To this end, we offer a structural taxonomy of metaheuristic search methods. This taxonomy serves as a framework for constructing and evaluating metaheuristic approaches from a general structural perspective as well as for conducting empirical research regarding the effectiveness of more detailed structural components. Implementation mechanisms of the detailed components within each structural component are left for future taxonomy research and development.
Money supply, inflation and economic growth in Libya

The aim of this study is to examine the relationship between money supply, inflation and economic growth in Libya. Vector auto-regression model, Johansen co-integration test and Granger causality were used in the analysis for the sample period of 1960-2016. The results indicate that all the variables are co-integrated in long-term. Furthermore, the increase in economic growth by 1% decreases inflation by 1.55%. While the growth in money supply by 1% will increase the price level by 1.15%. According to the results of the causality test, there is no causality direction in short-run among the variables except unidirectional causality among economic growth and money supply running from RGDP to RM2 at the to 5% significance level. In addition, the response of inflation on the economic growth is negative all throughout the ten periods. Also, the same applies to money supply and economic growth. Besides, economic growth has an early and positive impact on money supply.
Towards Development of a Cyber Security Capability Maturity Model (CSCMM)

Business processes are increasingly becoming technology driven. Disruptive technologies like social, mobility, analytics and cloud (SMAC) and internet of things (IOTs) have made a paradigm shift in the information technology space. With this evolution, the cyber security threats to digital infrastructure also have been increasing manifold and are a concern for the sustenance of business growth scenario. There is a need for a continuous improvement program in the cyber security posture of the organisations to keep pace with the increasing threats. Capability maturity models help to achieve this purpose. This paper presents the formulation and validation of a new cyber security capability maturity model (CSCMM) by comparing the strengths and limitations of nine contemporary maturity models and performing an empirical analysis of inputs from 200 odd relevant cross industry sector professionals. CSCMM is expected to improve the cyber security posture of the organisations to combat to the new generation threats.
The Impact of Innovation Strategy on the Financial, Social and Environmental Performance

The core purpose of this study is to investigate the effect of innovation strategy on financial, social and environmental performance of companies listed on the Tehran Stock Exchange (TSE). The information used is from 129 companies listed on TSE in different industries between 2011 and 2018 (1032 observations). In order to analyze the data, a multivariate regression test was used. The results showed a positive and significant relationship between innovation strategy on financial performance and environmental performance. Also, the relationship between innovation strategy and social performance has a positive but insignificant. Innovation tools are also among the few management tools that can have a positive impact on both financial performance and the company’s environmental performance. In this research, an attempt has been made to look at the idea of innovation from a financial point of view, and its results in the long run indicate the right choice of management to invest in the company’s research and development unit.
Levels of Ontology and Natural Language

Two levels of ontology are commonly distinguished in metaphysics: the ontology of ordinary objects, or more generally ordinary ontology, and the ontology of what there really or fundamentally is. This chapter argues that natural language reflects not only the ordinary ontology but also a language-driven ontology, which is involved in the mass-count distinction and part-structure-sensitive semantic selection (as well as perhaps the light ontology of pleonastic entities in the sense of Schiffer). The language-driven ontology does not constitute another level of representation, but is taken to be a (selective) ontology of the real, given a plenitudinous or maximalist conception of reality. The language-driven ontology aligns closely with the functional part of grammar and a commitment to it is mandatory with the use of language. This gives rise to a novel view according to which part of ontology should be considered part of universal grammar on a broadened understanding. The chapter recasts the author’s older theory of situated part structures without situations, in purely ontological terms, making use of a primitive notion of unity.
A performance analysis of stereo matching algorithms for stereo vision applications in smart environments

Stereo vision is a subfield of computer vision that tends to an essential research issue of reproducing the three-dimensional directions and focuses for depth estimation. This paper gives a relative investigation of stereo vision and matching techniques, utilised to resolve the correspondence problem. The investigation of matching algorithms is done by the use of extensive experiments on the Middlebury benchmark dataset. The tests concentrated on an examination of three stereovision techniques namely mean shift algorithm (MSA), seed growing algorithm (SGA) and multi-curve fitting (MCF) algorithm. With a specific end goal to evaluate the execution, some statistics related insights were computed. The experimental results demonstrated that best outcome is attained by the MCF algorithm in terms of depth estimation, disparity estimation and CT. The presented MCF algorithm attains a minimum computation time (CT) of 2 s whereas the other MSA and SGA require a maximum CT of 8.9 s and 7 s, respectively. The simulation results verified that the MCF algorithm reduces the processing time in a significant way than the compared methods.
Efficient Road Traffic Control Using CPU Scheduling Algorithms

The aim of this study was to find an efficient traffic control algorithm that reduces the total waiting time (TWT) of all vehicles in a traffic jam at a two-lane intersection.This paper draws from time-shared computer systems and the scheduling theory to determine the optimum algorithm for scheduling traffic.In this study, four CPU Scheduling algorithms were evaluated against a set of 100 randomly-generated traffic patterns in a simulated environment to determine their relative levels of efficiency in reducing the TWTs of vehicles in a traffic jam and identify the most efficient one.The study showed significant differences in the performance of each algorithm, with the Shortest Job First Context Switch and the Shortest Job First algorithms being the most efficient of the four algorithms evaluated.This paper also includes recommendations for an efficient traffic control system which uses cameras placed at intersections and the CPU Scheduling Algorithms described in other sections of this paper to efficiently control traffic.The system can be implemented with minimal modifications to existing infrastructure, and would reduce traffic delays at congested intersections.This would provide social, environmental and economic benefits, some of which were discussed in this paper.
Author Profiling u sing Stylistic a nd N Gram Features

The World Wide Web is increasing tremendously with massive amount of textual content primarily through social media sites. Most of the users are not interested to upload their genuine details along with textual content to these sites. To identify the correct information of the authors the researchers started a new research area named as Authorship Analysis. The authorship Analysis is used to find the details of the authors by examining their text. Authorship Profiling is one type of Authorship Analysis, which is used to detect the demographic characteristics like Age, Gender, Location, Educational Background, Nativity Language and Personality Traits of the authors by examining writing skills in their written text. Stylometry is one research area defines a set of stylometric features namely word based, character based, syntactic, structural and content based features for differentiating the author’s writing styles. In this work, the experimentation conducted with various stylistic features, N-grams and content based features for gender prediction. These features are used for representing the vectors of documents. The classification algorithms produce the model by processing these vectors. Two classification algorithms namely Random Forest, Naïve Bayes Multinomial were used for classification. We concentrated on prediction of Gender from 2019 Pan Competition Twitter dataset. Our approach obtained best accuracies when compared with many Authorship Profiling approaches.
Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion

Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era [10][20]. To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.
Gender Classification for Emotional Speech using GMFCC and Deep LSTM

We have come to the point that one of the important aspects of the process speech emotion recognition is the gender classification. The correct classification of gender will improve the performance of Speech Emotion Recognition (SER) system towards its robustness. Here, we are specifically referring to Gammatone Mel Frequency Cepstral Coefficient (GMFCC) as a feature extraction method that extracts features from IITKGP-SESHC dataset, which is very crucial in deciding either male or female in gender classification. The well known classifier “Deep Long Short Term Memory (Deep LSTM)” is itself an important kind of Recurrent Neural Network (RNN) that handles the long-range dependencies more efficiently than the RNNs. The GMFCC feature has to pass through the Deep LSTM and get average percent gender identification accuracy of 98.3%.
Internet Forums in Class: Innovation for Science Education by Focusing on Social Media

Internet forums are a part of the World Wide Web and are frequently used to ask questions and receive answers. They are popular platforms for exchanging information and comparing opinions. Many people query and retrieve information from Internet forums on various topics, many of which are related to science and technology. These include health, cosmetics, technical devices, and environmental issues. The reliability of the corresponding entries in Internet forums, however, should be approached with great caution. Answers found in general Internet forums are often provided by laypeople. Forum posts may contain information without scientific proofand are influenced by personal beliefs and opinions. This paper sums up the action research journey of a science teacher who looked into how secondary school students view and use Internet forums and how such Internet forums may be potentially useful for innovation in science teaching. The project encompassed different action research measures. It suggests that it is potentially beneficial for teaching to more thoroughly focus on social media in science education, like Internet forums. Such media provide examples of content and learning tools for science teaching, which can promote learning and develop critical (scientific) media literacy.
Bot Management

Dialogflow not only provides great tools for writing conversations and bot building but it also provides features for bot management. Agent Validation and Agent Training are such features. It makes life much easier for conversational designers who are building complex bots in an enterprise environment.
Humanization of the educational space – 2021: Digitalization of the educational environment (Overall results of the international forum)

The results of the international forum “Humanization of the educational space – 2021: digitalization of the educational environment”, which was held on February 18–19, 2021 in Saratov, are presented. The conference was organized by the Federal State Budgetary Educational Institution of Higher Education “Saratov National Research State University named after N. G. Chernyshevsky”, the Faculty of Pedagogical and Special Needs Education; Federal State Budgetary Scientific Institution “Institute for the Study of Childhood, Family and Education of the Russian Academy of Education”; Baranovichi State University (Republic of Belarus); West Kazakhstan Innovation and Technological University (Republic of Kazakhstan); Fergana State University (Republic of Uzbekistan).