Reviewerly/exHarmony huggingface dataset:
Machine Learning for Security of Cyber-Physical Systems and Security of Machine Learning: Attacks, Defences, and Current Approaches

The scope of cyber-physical systems (CPS) is extending and is currently being utilized in many critical infrastructures. Any sort of security reach in such infrastructures could trigger human as well as financial loss. So, the security of these systems is of utmost importance. The feasibility of many approaches has been explored by researchers to ensure the security of CPS, one such approach is based on intelligent methods like machine learning (ML) and deep learning (DL). The involvement of intelligent methods also extends the attack surface of CPS, so there is a need to make them secure as well because they are also vulnerable to many types of adversarial attacks. Thus in this work, we review the existing approaches of adversarial attacks like Momentum-based, GAN-based, Fast Gradient Sign Method, Momentum Iterative Fast Gradient Sign Method, the variant of Natural Evolution Strategies, zero-order (ZOO) random position ascent method, Deep fool, and many more. Moreover, we also explored some defence strategies like Modifying Data, Modifying Models, Using Auxiliary Tools, etc., that could be utilized to prevent adversarial attacks. Moreover, we have proposed a methodology for ensuring the security of CPS and the security of defender (intelligent model, i.e. ML and DL) itself.
Software Project Management Myths

Software Project Management is a topic that is rarely found in the call for papers at Software Engineering conferences. This combined with the fact that most software project managers have not been trained in management methods has resulted in software project managers adopting methods that they believe are beneficial but are not. This is not a sustainable situation as software projects are becoming more expensive with far reaching consequences for failure. This paper cites some of the more common fallacious methods and provides published references refuting their presumed benefits together with suggestions as to how to improve the current state of affairs.
Applications of Muli: Solving Practical Problems with Constraint-Logic Object-Oriented Programming

The Münster Logic-Imperative Language (Muli) is a constraint-logic object-oriented programming language, suited for the development of applications that interleave constraint-logic search with deterministic, imperative execution. For instance, Muli can generate graph structures of neural networks using non-deterministic search, interleaved with immediate evaluation of each generated network regarding its fitness. Furthermore, it can be used for finding solutions to planning problems. In this paper, we explain and demonstrate how these application problems are solved using Muli.
Strategies in Conditional Narrowing Modulo SMT Plus Axioms

This work presents a narrowing calculus that uses strategies to solve reachability problems in order-sorted conditional rewrite theories whose underlying equational logic is composed of some theories solvable via a satisfiability modulo theories (SMT) solver plus some combination of associativity, commutativity, and identity. Both the strategies and the rewrite rules are allowed to be parameterized, i.e., they may have a set of common constants that are given a value as part of the solution of a problem. A proof tree based interpretation of the strategy language is used to prove the soundness and weak completeness of the calculus.
An Enhanced GLCM and PCA Algorithm for Image Watermarking

Digital image watermarking is a technique to provide safety and authentication to sensitive data in the form of images. It prevents unauthorized access and misuse of the data. In today’s world of the Internet, where there is a high risk of sensitive data being misused, it is essential to take some measures to ensure data safety. In the proposed technique, features are extracted and analyzed from the image that must be protected by watermarking. Then the watermark is embedded into this cover image to get the final watermarked image. We have used GLCM and PCA algorithms to improve the efficiency of the watermarking scheme to attain maximum security. This technique is less complex and performs well. With this technique, a blind watermark is generated, which is more robust against attacks and thus improves the security of confidential data. We have used the parameters PSNR and MSE to test the efficiency of the proposed technique. We have used MATLAB for simulation.
Comparison of Debris Removal in Pap-Smear Images Using Enhanced Erosion and Dilation

The pap-smear test is considered one of the most common methods available for cervical cancer screening. Women above a particular age are supposed to undergo the cervical screening procedure at least once a year to identify whether there is the presence of cancerous cells. Since the manual screening of each cell from a pap-smear slide is tedious and time-consuming, automated pap-smear analysis is the need of the day. In the slides, during automated analysis, the presence of unwanted materials such as bacteria and blood particles may produce a false diagnosis. The cell structure is one of the main challenges faced during the process. Usually, debris removal is performed by many researchers along with segmentation. While enhancing the images debris removal can be performed. This can improve the quality of enhanced images. Therefore, debris removal plays a very important role in the automated pap-smear analysis. In this paper, debris removal is performed using enhanced erosion and dilation along with the image enhancement which makes it easy for the subsequent processes. The debris removal methods are compared by using two different binarization techniques. Initially, the images are preprocessed using diffusion stop function-based CLAHE algorithm, and then, the images are undergone kittler binarization. Mathematical morphological operations like erosion and dilation are applied to the image to remove the debris. Next, instead of kittler binarization, Otsu’s method is used. The results are evaluated with the performance measures sensitivity, specificity, and accuracy. From the results obtained with a sensitivity value of 99%, specificity of 99%, and accuracy of 98%, it is clear that the first method i.e., kittler binarization with morphological operations gives better performance.
A Study on the Integration of Different DLP Systems at Different Levels

Individuals and organizations worry more about leaking confidential information. In the past, data privacy was safeguarded by security rules and techniques like firewalls, VPNs, and IDSs. These systems aren't proactive or committed to protecting sensitive data, so rules must be implemented to do so. Confidential data can leak out in a variety of ways and through a variety of channels, which can have major impact. Therefore, there has been a push to use better systems to solve these issues. Data leakage prevention (DLP) Systems are meant to discover and halt the leaking of confidential details when the data is in use, in motion, or at rest. To detect data leaks and put a stop to them, DLP Systems employ a variety of approaches for analysing the content and context of confidential data. In this work, we have conducted a detailed survey on the various DLP Systems mechanisms that are in use. It gives a clear definition of DLP Systems and classifies the several active research directions in this area. In addition, we provide prospects for the development of more consistent DLP Systems that can compensate for some of the shortcomings of the ones that are existing now.
Software Complexity Prediction Model: A Combined Machine Learning Approach

The need for computers increased quickly. As a result, the program is utilized in a significant and intricate manner. More complex systems are being developed by software businesses. Additionally, customers expect great quality, but the market requires them to finish their assignment faster. Different measuring methods are employed by software firms. Some of these include customer feedback after it has been given to customers, software testing, and stakeholder input. The objective of this project is to use a combination of machine learning techniques to predict software bug states using the NASA MDP dataset. The research process considered data preprocessing methods and applied singular and combination machine learning algorithms. To create the model, the single classifiers were combined using the voting method. Accuracy, precision, and recall were used to evaluate the model's effectiveness, along with tenfold cross-validation. The promising result was recorded by a combination of J48 and SMO classifiers. Before attempting to test the software product, the researcher retrieved attribute data from the source code; the complexity of the software product will then be ascertained using the constructed model. The main contribution of this study is to improve software quality by incorporating a machine learning framework into the present software development life cycle between implementation and testing.
A Systematic Study of Various Approaches and Problem Areas of Named Entity Recognition

Named entity recognition (NER) is an essential part of the natural language processing (NLP) pipeline for the extraction of information as it finds application in many downstream applications. Many different rule-based, statistical, probabilistic, or hybrid approaches have been used to address it, the recent ones being machine or deep learning. Due to fast-changing technologies, many new developments have emerged in NER as well. This paper aims to provide a brief introduction, an overview of the new advancements, the addressed problem areas and approaches for the duration of five years from 2017 to 2021. This period has seen advances in deep learning, and some major developments are seen such as contextual embedding. The study explores different problem areas identified such as performance, handling noise or robustness, nested entities, low resources in new languages, scalability, fine-grained entities, and others being worked upon using supervised, unsupervised, reinforcement transfer learning working at character, morphological, word or sentence level, or by adding dictionaries for including knowledge. The paper can help provide a high-level input, to give an idea, of the areas that are currently being worked upon in NER along with the latest approaches with their advantages and future directions.
Survey of Text Summarization Stratification

The volume of data on the Internet has increased at an exponential rate during the previous decade. Consequently, the need for a method for converting this massive amount of raw data into meaningful information that a human brain can comprehend emerges. Text summarization is a common research technique that aids in dealing with a massive quantity of data. Automatic summarization is a well-known approach for distilling the important ideas in a document. It works by creating a shortened form of the text and preserving important information. Techniques for text summarizing are classified as extractive or abstractive. Extractive summarization methods reduce the burden of summarization by choosing a few relevant sentences from the original text. The implications of sentences are calculated using linguistic and statistical characteristics. This paper investigates extractive and abstract methods for text summarization. We will also explore many efforts in automatic summarization, particularly recent ones, in this article.
Monitoring False Colour in an External Monitor for Film and Television Production: An Evaluative Study

Today, video production monitors are integral to television, broadcasting, and film production. In fact, because monitors accurately display what the camera sees without any changes or enhancements, they deliver a realistic image essential for video production. Though a camera offers a suggested exposure setting, images demand greater exposure accuracy. It is therefore essential to understand the camera’s exposure level. Underexposure shots tend to lack clarity and overexposure causes in advertent loss of detail in image’s highlights. The challenge thus is in setting the correct level of exposure to convey a message. An external monitor has certain distinguishing features that enhance picture quality in several ways. One such feature is ‘false colour’, which is found primarily on external monitors. False colour benefits cinematographers by transforming varied exposure levels in a frame into distinct bands. They are allowing the user to see where under or overexposure is occurring. It exposes faults that a built-in internal camera viewfinder may easily ignore while minimizing overheating issues. This paper undertakes a comparative analysis of the picture quality in terms of colour, brightness, and contrast of an external monitor with the camera’s inbuilt viewfinder. This is an experimental study that determines the amount and quality of exposure that an image receives. Furthermore, this research experiments with ‘false colour’ viewing in terms of the IRE value chart and examines the exposure levels of all parts of the shots before recording. In this research, a stunning and perfectly exposed image was made by adjusting the false colours according to the IRE value. Underexposed areas appeared in blue, and overexposed areas in red. Checking on the skin tones faster is the key to exposing the image quickly and accurately with the help of false colour.
Clinical Named Entity Recognition Using U-Net Classification Model

Named entity recognition (NER) is a commonly followed standard approach in natural language processing. For example, properly recognize the names of people, sites and establishments in a sentence; or some domain-specific nouns, specific objects, etc. Consequently, the natural language processing domain can more effectively tackle complex tasks, such as question answering, information extraction and machine transformation. The deep learning models which are already proposed are quite complicated and need high execution time. In this research work, the clinical entity recognition technique is proposed which is based on the three phases: pre-processing, boundary detection and classification. The approach of U-Net is applied proposed for the classification for the named entity recognition. The proposed model is implemented in Python, and results are analyzed in terms of accuracy, precision and recall. The proposed model shows approx. 5% improvements in the results for the entity recognition.
Blockchain Framework for Automated Life Insurance

The insurance process is done manually in India. The insurance client has to depend on an insurance agent from buying to the claim firing process which leads to wrong entry, fraudulent claims, and cost overhead on the agent’s commission. An insurance client has to maintain documents and make them available at the time of claim firing, which is very cumbersome. Blockchain smart contracts will provide a secure, automatic, cost-cutting, paperless, and real-time insurance process. This paper aims to provide a blockchain solution for life insurance claim processing, as it is the most purchased and needy insurance. An architectural illustration of a blockchain prototype is contributed to this paper. As a result, the authors found that using Hyperledger Composer, we can develop fine-grained insurance applications.
Blockchain for IoT: A Comprehensive Review for Precision Agricultural Networks

Internet of Things (IoT) has gained popularity in recent years due to the services it offers and its wide usage in the field of science and technology such as smart agriculture, smart home devices. We now live in a world surrounded by smart gadgets and utilize it almost on daily basis. As the usage is increasing, the amount of data being disseminated is also increasing due to which a lot of data is being exposed to threats. So, it must be made sure that the channels through which data is being transferred should reach securely to the endpoint without compromising integrity, confidentiality, and authentication, ensuring that data reached should not be modified or tampered. In recent years, an important technique has been developed called “blockchain”, which can help in improving security, trust, speed, visibility, immutability, and traceability. Motivated by these facts, this work explores the potential of the blockchain and the efficacy of using it to deal with growing security and performance challenges in IoT associated within precision agriculture.
Effect of Anime on Personality and Popularity of Japanese Culture

Anime is the unique art animation shows or movies originating from Japan. It has received widespread appreciation and recognition from the public. It is an aggregation of multiple genres and will always have something for everyone. Due to its wide popularity and ability to grasp attention, it is often the talk of the town and has led to the genesis of many communities, each based on one/multiple anime shows/movies. Observation shows that consumption of such media has led many to relate to a particular character from the show and revolve their personality around said character. They try to imitate various behavioral habits, tones, and even dialogs from these characters. Many people have begun using various Japanese terms and dialogs in natural speech. Unconsciously, anime has started to affect its watchers’ natural behavior, speech, and vocabulary. This paper aims to study the range of effects of anime on an average watcher. We will also learn how anime has led to a significant increase in the knowledge and appreciation of Japanese culture and how it could affect tourism in the recent years.
The Proof of Authority Consensus Algorithm for IIoT Security

Blockchain-based technologies have been created across a range of businesses for improving data security and integrity. A network chain is the most remarkable blockchain applications as part of the Industrial Internet of Things (IIoT). Sensor data in real time aids industrial equipment and infrastructures in making decisions and taking specified actions. Without sufficient security, an IIoT system can cause operational disruption and financial loss and lead to malicious activities and system manipulation. Detecting anomalies in IIoT networks is critical for network security. In this paper we discuss the Proof of Authority consensus algorithm. The Proof of Authority (PoA) depends on the reputation of trusted parties in blockchain network. The PoA consensus mechanism is based on the values of identities on a network, and validators stake their own identities and reputation. So, the validating nodes that are randomly picked as trustworthy safeguard the Proof of Authority Blockchain network. In Proof of Authority model, transactions are reviewed by already approved network users, and it operates with a fixed number of block validators. Since the identities of the nodes are trusted and known, the process can be used in applications like supply chains and trade networks.
LSTM-Based Encoder–Decoder Attention Model for Text Translation and Simplification on the Constitution of India

Natural language processing techniques can be used on judicial and legislative documents like the Constitution for making it more accessible to the general audience. Various approaches such as Natural Machine Translation (NMT), text simplification of complex sentences can be performed on the Constitution. The model proposed in this paper can be used for the purpose of translation and simplification of the Constitution of India. The model is a LSTM variant forming an encoder–decoder network integrated with an attention layer and is trained using teacher forcing method. The data set used for the task of translation consists of Parallel Corpus from IIT Bombay English–Hindi concatenated with our own curated data set of 300 English articles of the Constitution of India translated to Hindi, whereas for simplification task it consists of our own curated data set of complex 300 English articles of the Constitution of India translated to simplified sentence. The proposed model for translation has a BLEU score of 15.34, and the research has elaborated on the performance analysis of the generated outputs and the BLEU score. For simplification, the results show some inconsistencies which can be improved by increasing the data set for simplification task.
Handwritten Character Evaluation and Recommendation System

Character recognition is a very popular machine learning application for example—OMR sheets are commonly used for grading purposes all over the world. It is a procedure of analysing human-marked data from documents like surveys and tests. The COVID-19 epidemic has had a negative influence on schooling, since it has caused students’ handwriting habits to deteriorate significantly. As a result, the goal of this paper is to propose a lightweight deep learning model that uses computer vision to aid in the practice, evaluation, and analysis of nursery students’ handwriting by evaluating sheets and pointing out major errors with an accuracy score that can be used to measure improvement. Recognition, evaluation, and analysis are the three primary components of the suggested paradigm. The steps are as follows: (1) Pre-processing, (2) deep learning model for feature extraction and classification, (3) predicting the letters in the supplied picture, (4) calculating the similarity index between the input image and the glyphs in the computer font, (5) matching the input image's stroke length and angles to font glyphs, and (6) displaying the character's score and analysis. The most accurate model for this process was 83.04%.
Exploiting Parts of Speech in Bangla-To-English Machine Translation Evaluation

Machine translation (MT) converts one language to another automatically. One of the major challenges of MT is evaluating the performance of the system. There are many automatic evaluation metrics available these days. But the results of automatic evaluation metrics are sometimes not reliable. In this paper, we have attempted to address this issue by considering another type of evaluation strategy, i.e., syntactic evaluation in Bangla-to-English translation. We have attempted to address the problems of automatic evaluation metric BLEU and, thereby, how syntactic evaluation could be helpful in achieving higher accuracy is discussed. In our syntactic evaluation, we have exploited the use of parts of speech (POS) during computing evaluation scores. A comparative analysis is done on different types of evaluations such as syntactic, human, and automatic on a low-resourced English–Bangla language pair. A correlation indicates syntactic evaluation score correlates more with the human evaluation score compared to the normal BLEU score.
A Novel Approach to Acquire Data for Improving Machine Learning Models Through Smart Contracts

Despite the Big Data Revolution, critical aspects to improve machine learning models have been overlooked concerning the benchmarked datasets and their nature that are available for the model. In this work, we propose a blockchain-based decentralised, trustless platform using “Smart Contracts” that is tailored exclusively for data collection from proficient developers and machine learning model improvement. Elastic Weighted Consolidation is used to update this model to take into account the characteristics of the incoming dataset(s) in order to avoid catastrophic forgetting, which occurs when a model only learns from fresh data and ignores its existing knowledge. A rewarding mechanism has been discussed, which ensures that low-quality data is not rewarded and good information is compensated fairly based on the improvement made to the model. It fosters a favourable environment for competition. This platform is conceived as a marketplace that provides monetary incentives for developers to partake in improving and contributing to model development.