Reviewerly/exHarmony huggingface dataset:
A Statistical Model for Automatic Error Detection and Correction of Assamese Words

Digitization of local languages is getting importance in the present scenario and the Language Processing task is also becoming popular among the Linguistic and IT people. It is very common that most of the people are comfortable with their native mother tongue. Writing of corrected word-form is also an important task in the digital platforms for the future existence of a language. In this research work, the Assamese language is taken as a Natural Language which is processed in the experiments. The Assamese language is one of the Indian languages and the research &amp; development of the Assamese language is going on; from the computational point of view, Assamese is in the development phase. In Assamese, there are some similar characters which are phonetically same but their glyphs are different these characters or symbols often cause confusion to the users while writing, these types of characters are specially taken into consideration in this research work. A list of 14 confusing characters pairs of Assamese letters is taken for experimental purpose. In addition, this research work has focused on errors of Assamese words, which are checked by using bigram and trigram models. Moreover, the proposed model also tries to find the erroneous character which causes the incorrectness and shows the suggestions for that incorrect character. A score based system is designed for the Assamese characters and each character is assigned a score from their probability of occurrences by using bigram and trigram language models. Different types of experiments are performed to check the correctness of the Assamese words and the proposed model is able to check the correctness of the Assamese word with accuracy ranging from 81% to 86%. Error rate in Assamese can be reduced by using this model in any digital platform where a user can type in Assamese.
SE-SqueezeNet: SqueezeNet extension with squeeze-and-excitation block

Convolutional neural networks have been popularly used for image recognition tasks. It is known that deep convolutional neural network can yield high recognition accuracy while training it can be very time-consuming. AlexNet was one of the very first networks shown to be effective for the tasks. However, due to its large kernel sizes and fully connected layers, the training time is significant. SqueezeNet has been known as smaller network that yields the same performance as AlexNet. Based on SqueezeNet, we are interested in exploring the effective insertion of the squeeze-and-excitation (SE) module into SqueezeNet that can further improve the performance and cost efficiency. The promising methodology and pattern of module insertion have been explored. The experimental results for evaluating the module insertion show the improvement on top1 accuracy by 1.55% and 3.32% while the model size is enlarged by up to 16% and 10% for CIFAR100 and ILSVRC2012 datasets respectively.
Expected Runtime Analyis by Program Verification

This chapter is concerned with analysing the expected runtime of probabilistic programs by exploiting program verification techniques. We introduce a weakest pre-conditioning framework á la Dijkstra that enables to determine the expected runtime in a compositional manner. Like weakest pre-conditions, it is a reasoning framework at the syntax level of programs. Applications of the weakest pre-conditioning framework include determining the expected runtime of randomised algorithms, as well as determining whether a program is positive almost-surely terminating, i.e., whether the expected number of computation steps until termination is finite for every possible input. For Bayesian networks, a restricted class of probabilistic programs, we show that the expected runtime analysis can be fully automated. In this way, the simulation time under rejection sampling can be determined. This is particularly useful for ill-conditioned inference queries.
Pre-eminent Strategy for Effective Utilization of Power in Data Center

Cloud computing is generating billions of dollars in revenue annually with very minor indication of slowing down. Today, most innovative technology is cloud-centric by retrofitting the technology to on-premises. Every IT business is also rushing from cloud-first to cloud-only concept. Data center are accounting for billions of kilowatt hours of electricity consumption every year. Huge costs spend for energy consumption than establishing the data center Infrastructure. In this paper we discuss some of the best practices to decrease energy consumption in data center .We can better control the things when we can measure it. So we also focus on seizing and measuring the energy needed for a typical data center. There is also environmental concern to be taken care with respect to data center because of CO2 Emission in cooling system.
Effectiveness of Electronic Voting Machine in the Electoral System of India: New Opportunities and Challenges

To ensure the privilege of voting rights of the citizens, the procedure of election with security and integrity is a basic condition of any nation. Voting in India is a constitutional and fundamental right under the Representation of People Act 1951 and under Article 19(1)(a) of the Constitution of India. The Elections in India are performed almost completely using electronic voting machines (EVM) build up over the past 2 decades by a pair of government-owned companies. However, in India these devices is known as Electronic Voting Machines ,have been praised for their easy devise, ease of use, and reliability, but recently they have also been criticized and despite this criticism, several particulars of the machineries’ design have never been publicly disclosed, and they have not been subjected to a rigorous, independent security evaluation. This research paper will evaluate the machine’s effectiveness and function in detail, as well as address its safety in light of related election procedures. Presently , in India available Electronic Voting Machines(EVM) has been applauded for their uncomplicated design, dependability as well as usability, yet as of late they have moreover been condemned taking after across the board reports of race abnormalities. In spite of this feedback, numerous subtle parts of the machines' outline have never been freely unveiled, as well as they have not been subjected to a thorough, autonomous security assessment. However, the whole world efficiently dismisses the electronic voting machine (EVM) and in fact, just a couple of nations are presently using the electronic voting machines (EVM). The research paper examines the effectiveness of the EVMs in the electoral system of India with new challenges and opportunities.
Quantile Factor Models

Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distributions are established using a kernel-smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. QFA estimation remains valid even when the idiosyncratic errors exhibit heavy-tailed distributions. An empirical application illustrates the usefulness of QFA by highlighting the role of extra factors in the forecasts of US GDP growth and inflation rates using a large set of predictors.
Algorithm of the Cyclic-Order Graph Program (Implementation and Usage)

C++ Implementation of the Cyclic-Order Graph Algorithm is presented together with the source code.This program helps in determination of crossing numbers for join products of graphs with paths of given numbers of vertices necessary for the proofs in the Graph Theory.Comparison with MATLAB (GNU Octave) is provided together with outlooks for the future development.
Sentiment Research on Twitter Data

The scale of social network data that is being generated is increasing exponentially day by day. Public and private opinion of various subjects or issues are expressed in social media. Sentiment analysis is a method of analyzing the sentiment of a statement that it embodies. Twitter is one of the social medias that is gaining popularity nowadays and most people are using this platform to express their opinions. Sentiment analysis on Twitter is an application of analyzing the sentiment of twitter data(tweets) conveyed by the user. The research on this problem statement has grown consistently. The main reason behind this is the challenging format of tweets that are posted, and it makes the processing difficult. The tweet format would be the number of characters, slangs, abbreviations, emojis, http links and so on. In this paper the aim to describe the methodologies adopted, the process and models applied, along with a generalized approach using python. Sentiment analysis aims to determine or measure the attitude of the writer with respect to some topic.
Implementasi Artificial Neural Network (ANN) Backpropagation Untuk Klasifikasi Jenis Penyakit Pada Daun Tanaman Tomat

Tomato is a horticultural plant that can be easily found in Indonesia. Tomatoes are popular for public consumption because their nutritional values have excellent content for health, besides tomatoes are widely cultivated because the price is quite stable. However, there are obstacles in tomato plants cultivation for farmers because tomatoes are vulnerability to diseases, so that farmers have difficulty distinguishing types of diseases that look similar. Therefore, this research was conducted to help farmers in identifying various diseases in tomato leaves. This research uses Artificial Neural Network (ANN) method with backpropagation algorithm to classify the types of diseases in tomato leaves. The data used for testing were 50 image data of bacterial spot disease, 50 image data of yellow leaf curl disease, and 50 image data of healthy leaves. Tomato leaves types of diseases classification with the best result that utilized backpropagation with Cross-validation 4 folds by following the rules with batch size of 100, hidden layers defined as , the learning rate for weight updates is 0.3, with validation threshold is used to terminate validation testing is 20, and 500 as the number of epoch to train through, have been resulted in an accuracy of 78% which required time 319.77 seconds to process data. Which was then assisted by Confusion Matrix as a tool to measure the performance of the classification results that have been carried out and precision results of 0.78 so as to obtain 117 data of true positive.&#x0D; Keywords: Tomato, Leaf imagery classification, Artificial Neural Network (ANN), Backpropagation.
Contributing Factors for Successful Information Security Management Implementation: A Conceptual Model

Information security management is a comprehensive information management technique that is used as a strategic approach for addressing risks, breaches of information security incidents that threaten confidentiality, integrity and availability. Information Security Management has become an important initiative for organizations to manage and protect their information assets responsibly and effectively. One of the common problems in organisations is the lack of guidelines for the implementation of an effective and efficient ISM. As a result, incidents and threats to organisations continue to rise causing the organisations to suffer losses and their reputation jeopardised. Therefore, this study aims to identify the success factors that could assist organisations in the implementation of ISM. The methodology used in this study is a qualitative research technique whereby a theoretical study was reviewed through existing literature together with ISM international standards, frameworks, guidelines, best practices and previous studies in the IS field. The data from the theoretical study was then analysed using content analysis. Twelve success factors were identified and the relationships between these factors are proposed. These factors are derived and grouped into aspects of people and process. Each factor contains its own element that represents either the role to play or the activity to perform. In the process aspect, the factor was further divided into the Plan, Do, Check and Act phases. The aspects and factors were then formulated as a conceptual model for information security management implementation. The conceptual model acts as a guideline and an initial setup for organisations intending to implement ISM in the future. Furthermore, it could also act as a reference for future research in the information security domain.
Recognition Technique of ConfidentialWords Using Neural Networksin Cognitive Infocommunications

score was not good.These results show that it is possible to predict confidential words; however, it is still challenging to predict paraphraseable words.
Deep Learning Based-Phishing Attack Detection

Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, e-commerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting good-quality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.
Design and Development of Pharmaceutical Company Information System Based on Website using the Waterfall Model

the pharmaceutical company is one of the companies which has an important role and has several activities such as manufacturing, distribution and main services to the community. This activity is controlled by the information systems department. Making information systems is needed to help pharmaceutical companies manage important data, so important data or less important data will be paperless. The design of this information system uses the waterfall method, where the system and design are very well designed before coding and testing operations are carried out. Then the data is stored using a database. Before doing a good design, research using business analysis, user needs, etc. After this information system is finished, the testing method uses blackbox. Testing methods are an important element of quality assurance that represents a fundamental review of specifications, design and coding. This testing technique also focuses on the software information domain. This pharmaceutical company information system was created to support the performance of a company. Making using a waterfall and testing using a blackbox, is expected to increase visability and meet the requirements of a software. The results of testing the device aims to look for errors in the category of functions that are not correct, the interface is not good or access from the database. Some parts are tested namely the home interface, user login, etc. Research shows that the waterfall method can be effectively used in making website-based information systems. After that the blackbox test shows if all the test components have been tested can be used very well
Future Research Directions

In previous chapters, we have present the big picture of existing personalized privacy protection solutions along with several privacy concerns and leading attacks. However, a mass of significant and prospective issues remain under-explored. The prosperity of machine learning, the Internet of Things, and blockchain brings appealing opportunities for researches on personalized privacy protection while posing further challenges in data utility upgradation in big data scenarios. Beyond this, there are numerous other topics that desiderata consideration in personalized privacy protection, and we outline several potentially promising research directions that may be worthy of future efforts.
Hybrid Quantum-Classical Key Distribution

Quantum Key Distribution (QKD) has been developed over the last decade; QKD addresses the challenge of a securely exchanging cryptographic key between two parties over an insecure channel where there are two parties that simultaneously generate and share a secret key using the polarization of quantum states of light by applying the phenomena of quantum physics. The integration of QKD protocol with public key cryptography for securely exchanging the encryption/decryption keys is proposed and simulated, the simulation results evaluate the work of the existing and proposed protocol taking into account different measures. Finally, a short security analysis is given to show the difference between the proposed protocol and its counterparts.
Reconstructing an S-box from its Difference Distribution Table

In this paper we study the problem of recovering a secret S-box from its difference distribution table (DDT). While being an interesting theoretical problem on its own, the ability to recover the S-box from the DDT of a secret S-box can be used in cryptanalytic attacks where the attacker can obtain the DDT (e.g., in Bar-On et al.’s attack on GOST), in supporting theoretical analysis of the properties of difference distribution tables (e.g., in Boura et al.’s work), or in some analysis of S-boxes with unknown design criteria (e.g., in Biryukov and Perrin’s analysis).We show that using the well established relation between the DDT and the linear approximation table (LAT), one can devise an algorithm different from the straightforward guess-and-determine (GD) algorithm proposed by Boura et al. Moreover, we show how to exploit this relation, and embed the knowledge obtained from it in the GD algorithm. We tested our new algorithm on random S-boxes of different sizes, and for random 14-bit bijective S-boxes, our results outperform the GD attack by several orders of magnitude.
Possibilities at the Intersection of AI and Blockchain Technology

Artificial intelligence and Blockchain are the most trending technologies these days, where artificial intelligence offers intelligent decision-making capabilities to machines which is similar to human beings and blockchain technology allows a decentralised pathway for encrypted data sharing between ledgers in a secured manner. Integration of both technologies forms a decentralised AI which enables the process of decision making on digitally encrypted platform for secure data sharing without involvement of any Third Party. This paper gives a detail on the possibilities of intersection of AI and Blockchain. The paper also contains the issues and problems related to the respective integration. An Algorithm is proposed in two parts, based on one of the given issues, which predicts the action plan of AI for destructing malware blocks in blockchain
The theoretical framework of factors influencing the pedagogical decision-making

The paper is based on the analysis and synthesis of scientific literature with the aim to disclose the peculiarities of pedagogical problem-solving as well as decision-making.It highlights the complexity of pedagogical decision-making.Most research of pedagogical decision-making concentrates on various constituents and psychological factors that are directly related to the features of teachers' personality.Some research of pedagogical decision-making focuses on the process of decision-making.Trying to answer the research question, this paper attempts to reveal the main groups of factors that influence the pedagogical decision-making.The theoretical framework, which is based on the insights concerning the pedagogical decision-making factors, is presented.
De-anonymizing Facial Recognition Embeddings

Advances of machine learning and hardware getting cheaper resulted in smart cameras equipped with facial recognition becoming unprecedentedly widespread worldwide.Undeniably, this has a great potential for a wide spectrum of uses, it also bears novel risks.In our work, we consider a specific related risk, one related to face embeddings, which are machine learning created metric values describing the face of a person.While embeddings seems arbitrary numbers to the naked eye and are hard to interpret for humans, we argue that some basic demographic attributes can be estimated from them and these values can be then used to look up the original person on social networking sites.We propose an approach for creating synthetic, life-like datasets consisting of embeddings and demographic data of several people.We show over these ground truth datasets that the aforementioned re-identifications attacks do not require expert skills in machine learning in order to be executed.In our experiments, we find that even with simple machine learning models the proportion of successfully re-identified people vary between 6.04% and 28.90%, depending on the population size of the simulation.
Copy and Move Detection in Audio Recordings using Dynamic Time Warping Algorithm

Copy and move forgery technique is a versatile technique used by criminals to change the evidences unlawfully. This is done by removing or adding the segments of an audio recording from another audio using simple software tools . Now-a-days in courts and forensics lab we use digital audio or speech as proof of evidence. With the advancement in digital software and technology it is made possible to modify the original audio data and tamper with it. In this paper we are using a robust method of copy and move detection using MFCC and Dynamic time warping which is robust against several kinds of regularly used post-processing actions and background noise, which highlights its promising potential of the suggested method as an audio tampering detection tool in various practical forensics applications with help of DTW. The proposed method is giving good outcome in differentiating the original sequences and forged sequences. This is done and implemented in mat lab.
Software Defect Prediction Analysis by using Machine Learning Algorithms

Programming deformation gauge expect a crucial activity in keeping up extraordinary programming and diminishing the cost of programming improvement. It urges adventure executives to relegate time and advantages for desert slanted modules through early flaw distinguishing proof. Programming flaw desire is a matched portrayal issue which orchestrates modules of programming into both 2 arrangements: Defect– slanted and not-deformation slanted modules. Misclassifying blemish slanted modules as not-disfigurement slanted modules prompts a higher misclassification cost than misclassifying not-flaw slanted modules as deformation slanted ones. The AI estimation used in this paper is a mix of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplace Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The proposed Algorithm is surveyed and demonstrates better execution and low misclassification cost when differentiated and the 3 calculations executed autonomously.