Reviewerly/exHarmony huggingface dataset:
Enabling Granular Exploration of Sequence Diagrams with Activity Diagrams

Visualization of program execution is a powerful means to comprehend program behavior. Sequence diagrams have long been used for representing the control flow of object-oriented programs. Since the sequence diagrams generated from run time can become large and unmanageable, several compaction measures are proposed to represent the diagram in a concise way. While the compact diagrams provide a summary of the execution, means to explore them in a systematic way are necessary. Hence, user-driven exploration methods have been proposed. One of them is the use of hyperlinks to unveil the depth of the sequence diagram on-demand. The compacted sequence diagram provides more flexibility to interactively explore the compacted regions in a hierarchical fashion. The user decides which regions to expand and up to what level of depth they must be expanded through the support of hyperlinks in order to comprehend the program. However, some of the crucial logic embedded inside the methods remain hidden. In this work, we show how an activity diagram can be constructed from the hyperlinked sequence diagram to portray the control flow logic at the sub-method level at different levels of granularity. Although the idea of an activity diagram for the entire execution can attract criticism owing to its large size, our interest is from the standpoint of portraying sliced executions on-demand. We contend that sliced activity diagrams can provide valuable insights into the execution by restricting the focus on the essentials. Finally, we show the utility through a variety of experiments.
Fraction of Data in a Secure Client-Server Communication Method Using Key Exchange

Secure communication is an interesting issue nowadays. While the fame of chat applications expands, this notoriety carries some security problems with it. An assortment of authentication mechanisms and encryption strategies recommended and applied between to limit the expanding security issues. Numerous analysts pick the best they found and use it in various mix to give security to the chat application. On the comparable terms, we have utilized a combination of key exchange calculation mixed with an encryption algorithm. This blend is alluded to as “Three-way system” since it guarantees all the three-protection plan of verification, data security and authentication, simultaneously. In this paper, we actualize a best in class client server hybrid model encryption framework utilizing DH algorithm combined with (AES) Advanced Encryption Standard encryption algorithm. Regardless of whether the key in transmission is cracked, the concept of Diffie Helman key exchange makes it futile, since key in transmission is of no utilization without client's private key, which is kept uniquely to the real client. This proposed design of three-way component makes it difficult for hackers to exploit the security framework, thereby securing the chat application.
Hate Speech detection in English and Malayalam Code-Mixed Text using BERT embedding

Hate speech detection is a very popular research area for past few years. Hate speech is given various definition by various researchers. In this paper we try to analyse the use of BERT embedding in hate speech detection in low resource language like Malayalam. The crucial challenge faced by researchers in this area are that most non-English languages are represented in code-mixed form in Social media. Here we work with transformer-based models to classify tweets as hate or non-hate content. Hence this is a novel approach that uses BERT in non-English text.
Secure Storage and Retrieval of Electronic Health Records

Electronic health records (EHRs) are crucial yet sensitive private information required in healthcare diagnosis and treatment, and must be regularly shared and exchanged with peers such as health care providers, pharmacies, researchers, patients, families and insurance firms. This makes it difficult to maintain track of a patient’s medical history. Storing and exchanging data across different institutions, as well as maintaining access control via several consents, only adds to the complexity of a patient’s treatment. Hence Electronic Health Records can be used and its secure storage and retrieval becomes a necessity. Our system introduces single encryption of patient details and double encryption of patient diagnostic details to securely store the data in the cloud. The system uses two of the strongest encryption algorithms, AES-256 and Blowfish for encryption.The approach uses ciphertext storage of medical records in the cloud, which is secured using a three-level access policy that allows patients to view their records, doctors to view their patients’ health records, and administrators to view all records.
Virtual World in Learning Disability

In terms of technological advantages, virtual reality or augmented reality remains less popular within the field of learning disabilities. Research shows that children with learning disabilities face various challenges in their day-to-day lives dealing with these disorders, demanding massive solutions. This chapter will address the pros and cons of virtual reality in learning disabilities across different age groups by combining theories of virtual worlds and learning disorders. Exciting research in virtual reality focuses on finding out how psychotherapies have benefits in learning and education. Upon review, it becomes evident that research in the virtual world along with learning disabilities has not yet been examined from a cohesive perspective, illustrating a lack of alliance that determines a more global understanding of the technological advantages of disabilities. Thus, this chapter aims to provide educators with an overview of explanations of the virtual world and to ensure appropriate development of VR/AR applications and special assistance for learning disabilities.
An Automated Kidney Tumour Detection Technique from Computer Tomography Images

The abnormal multiplication of cells in the kidney can lead to the formation of a tumor commonly known as kidney cancer. The early-stage diagnosis of tumors in the kidney can significantly improve the chances of recovery. There are several imaging techniques available to physicians to diagnose the stage of cancer and the response of the patients to the prescription. Various medical imaging techniques are extensively used for the diagnosis and detection of kidney tumors. Currently, the diagnosis and detection are the primary emphases of renal kidney cancer-related research besides recognizing whether the tumor is malignant or not. In this paper, CT images are utilized to spot and pinpoint tumor regions in kidneys using an image processing technique. The current image processing technique combines pre-processing, edge detection, and segmentation stages and anticipated a rapid diagnosis of tumor from CT scans. The available CT scan image can be transformed to a grayscale complement and subsequently subjected to noise reduction during the pre-processing stage. Various well-known algorithms are used in the second stage for detecting the edges. Finally, K means clustering and later on, K means segmentation is employed to distinguish the tumor grown region in the CT images of kidneys.
Analyzing Performance of Pre-trained Models in Detecting Sentiments of Code Mixed Manglish Text Reviews from Social Media

In the context of social media and multilingual community, code mixing is a common sociolinguistic occurrence. Analyzing code mixed texts from social media is a vital language processing task for applications such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation. Very limited code mixed datasets are available for low-resourced languages like Malayalam, Tamil, Telugu etc. The corpora available in the area of code switching contributed significantly to the advancement of research in this field. This work focuses on processing code mixed Mangish text reviews, which is a mixture of Malayalam and English languages, represented in Roman script. Also it evaluates the performance of different pre-trained models like mBERT, DistilBERT(multilingual), RoBERTa, XLM and XLNET to determine the sentiment class of Manglish text reviews from the Dravidian code mix repository and identify which performs better in the context.
Adaptive Control of Machines: A Data Driven Model Based Design

The paper presents the design, development, analysis of data driven model based control system of PMDC motor. Data is obtained from the motor using data acquisition techniques. The plant model is designed through supervised machine learning technique. Artificial Neural Network based motor model was developed. The obtained model was used to design a controller. The model along with its underlying neural network and the controller was developed with the help of MATLAB/SIMULINK platform.
Attendance Management System Using Facial Recognition

In a classroom, keeping track of attendance is a difficult and time-consuming task. Here comes the importance of automated techniques for attendance marking. Facial recognition is necessary for automated attendance tracking methods. This suggested study uses face detection algorithms to provide an attendance marking system. The primary goals of this endeavor are to accurately record attendance and speed up the procedure. This lessens peoples’ workloads as well. This Proposed system is implemented using 4 Steps such as Finding all faces, Posing and working faces, Encoding faces, Finding the person’s name from the encoding
Context-aware gender and age recognition from smartphone sensors

Smartphones include multiple sensors to track a device’s movement. This research investigated the capability of smartphone motion sensors to determine the user’s gender and age in different contexts. A subject’s context is an action they engage in, such as sitting or standing. This paper is based on the differences in behavior between male and female smartphone users, precisely, how they hold and manage their devices. To build our approach, we use the MotionSense Dataset. This dataset contains data from accelerometer and gyroscope sensors over time (attitude, gravity, acceleration, and rotationRate). In this study, we consider multiple contexts such as walking, sitting, standing, and jogging. Our method proposes to use smartphone sensors to detect an individual’s age and gender with an accuracy of 99.89% if they are seated.
Survival Analysis on Relapse Predictions of Breast Cancer using Optimized ANN and Deepsurv Model

Breast cancer is one of the widespread cancer types existing among women worldwide. The improvement in medical treatment and widespread awareness has reduced the mortality rate caused due to breast cancer. However, the patients have to face the next challenge, the relapse of breast cancer which may cause more severe effects leading to death. For some patients breast cancer may come back after treatment, sometimes even years later. This recurrence can be local, regional or in a distant area. The main aim of this research work is to predict the relapse of breast cancer among the patients using a machine learning approach. The proposed model also predicts the probability of relapse over time using a survival analysis approach. The data is obtained from Molecular Taxonomy of Breast Cancer International Consortium. It is a combination of clinical and molecular data of 1980 breast cancer patients. The clinical data comprises of basic clinical variables that are tumor grade, tumor size, patient age and number of lymph nodes positives. Molecular data includes segmented copy number alterations along 4794 consistent copy number regions. Artificial Neural Network is used to classify breast cancer patients as having a high or low risk of experiencing a recurrence. Survival Analysis is conducted on predicted high risk and low risk patient labels to predict the probability of relapse over time. The survival analysis is performed primarily using Linear Cox Proportional Hazards model. Further, the Deepsurv model is used to perform the survival analysis which outperforms the Linear Cox Proportional Hazards model. Deepsurv model generates individual and average survival analysis curves for predicted high risk patients. Artificial Neural Network predicts risk of breast cancer relapse with an accuracy of 0.95. The survival analysis models were evaluated by computing concordance index for the models. The Linear Cox Proportional Hazards model has a concordance index of 0.41. Further, the concordance index is improved to 0.71 using Deepsurv model.
Action Recognition to Sign Language Recognition using Machine Learning Techniques

Sign language communication has become a permanent remedy for those who are deaf or hard of hearing. It is a vital asset for people who cannot express their opinions and feelings to the rest of the world. It smooths out and simplifies the combination cycle between them and others. In any event, simply inventing gesture-based communication isn’t enough. This gift comes with a slew of surprises. The hand signs are frequently mixed up and misinterpreted by someone who has never known or learned sign language, and some people may know it but use it in a different language. In any case, the long-standing correspondence gap can now be narrowed thanks to the discovery of several ways for automating the recognition of sign signals. In this review, the client should be able to take photographs or video input of the hand signals using a camera, and the framework should anticipate the name of the hand sign from the captured picture.
Developing a Real time model to Detect SMS Phishing Attacks in Edges using BERT

Phishing is the enchanting utilization of automated trades to cheat and exploit clients. Phishing attacks exertion to get intriguing, private information, for instance, usernames, passwords, charge card information, and affiliation confirmations, absolutely. By acting like a real individual or foundation through telephone or email, electronic aggressors utilize social expecting to push occurrences toward performing unequivocal activities like tapping on a perilous collusion or affiliation or wilfully uncovering private data. Nowadays, aggressors use different correspondence mediums to talk with the adversities, for instance, email, message (SMS), telephone, and others. No matter what the quick movement of Internet show based illuminating affiliations, SMS genuinely remains an obvious correspondence relationship in our lives as of in the relatively recent past. For example, a few affiliations consider that messages are more convincing than messages. This is thinking about the way that 82% of SMSs are explored inside 5 min., yet clients simply open one of each four messages they get. The significance of generally suggests irritating or unconstrained messages got by phone clients through Short Messaging Service (SMS). The SMS phishing is another strategy where the phisher works the SMS as a medium to visit with individuals being suggested and this system is seen as smishing (SMS+phishing). In any case, SMS is one of the potential instruments to really chat with others through phones without the web. As Transfer Learning from colossal degree set up models ends up being more inevitable in Natural Language Processing (NLP), working these huge models in on-the-edge as well as under obliged computational arrangement or confirmation monetary plans stays testing. Phones are famous with engineers since they're expected fast responses pondering insignificant huge information. For seeing phishing attacks in low computational contraptions we can use a quantized model. This work focuses on seeing SMS phishing attacks continuously with the help of BERT in edges. Not the slightest bit like reliable language depiction models, BERT expected to pre-train basic bidirectional depictions from unlabeled text by customarily shaping on both left and right setting in all layers.
APPEARS Framework for evaluating Gamified Cyber Security Awareness Training

The Security Awareness in the Information Technology field is very important for IT professionals as their online interactions and presence need to be done in a secure manner. Information Security (InfoSec) or Cybersecurity IQ is a need of the hour for the average User regarding password strengths, Open Wi-Fi networks, etc. This study is focused on an evaluation model for Cyber Security Awareness Training (CSAT) by reviewing how gamification has an effect on security awareness as compared to traditional learning methods. Gaps were identified which led to the development of our model which assists administrators or organizations in evaluating the overall effectiveness of a CSAT program or environment. A conceptual model was developed called “APPEARS” which focuses on aspects like Agency, Proficiency, Punishment Feedback mechanisms, and more. Allowing a holistic assessment of any CSAT program. The evaluation can be used both internally and externally in the form of a trainer or conductor of training as well as a trainee. The APPEARS framework can address a host of questions from SMEs to large-scale organizations/enterprises by evaluating overall levels of CSAT across the entire organization. The framework can be used as a survey tool as well as an assessment tool. Further avenues for research were identified which will help get more concrete metrics on every individual component of the APPEARS framework.
Unpacking African Development Dynamics

At the heart of political economy is how politics affects the economy and vice versa. Society wants the supply of public services like healthcare, education, security, and infrastructure to be flexible, innovative, and efficient. However, the barriers created by bureaucracies often stifle innovation. The argument for harnessing the policy-market nexus to increase aggregate economic welfare remains compelling. While conventional arguments on the ability of the private sector to intrinsically generate efficiency gains remain valid, governments' traditional role of providing an enabling environment to foster private risk taking for capital accumulation is no less important. This chapter offers insights into the political economy of the public policy dynamics of the African economy and society, with emphasis on public-private collaborations for policy innovation. To break new ground and solve some of the most pressing challenges of our era, innovating the public policy cycle is key. Nowhere is the policy innovation imperative more compelling than in Africa.
Expedite the Process of Reconnaissance: Eagle’s Eye of Security

Reconnaissance is the process of gathering information, whether about a person or an organization. It plays a key role in identifying valuable attack surface during penetration testing through various way such as asset discovery, version identifications, software in use as well as some low-level threats which are also regarded as “low-hanging” vulnerabilities. The main focus will be on the passive reconnaissance. The importance of time and efficiency has increased, to meet this requirement it is crucial to adopt newer technologies. For instance, in this paper after discussing the methodology for passive reconnaissance, two of prominent tools in penetration testing, sublist3r and linkfinder are compared with their recreated made golang version. These are originally written in python which lacks behind in terms of speed when compared to the tool used here.
Combinatorial Optimization For Travelling Salesman And Vehicle Routing Problem

Heuristics are commonly used to solve combinatorial optimization problems such as the Traveling Salesman Problem and the Vehicle Routing Problem. However, heuristic implementation will take time in a variety of conditions and situations. We will employ deep Graph Convolution Networks to generate a well-organized graph characterization for the Traveling Salesman Problem, as well as to exclude auto-regressive tours utilising the most relevant beam search and synchronisation. For the Vehicle Routing Problem, we will use Local Re-writer, which is trained using Reinforcement Learning and learns to select heuristics using the policy and rewrite the current solution’s local component in order to improve repeatedly until the merger. These methods will outperform the classic way of heuristics with any standard search algorithm, providing the advantage of speed and efficiency. Instead of providing a solution that almost completes small scale graphs, these methods will provide an advanced solution for large graphs.
Analysis of Character Strengths from Handwriting Samples using Deep Learning

Many artificial neural models and deep neural models have been utilized for the prediction of personality using handwriting images. Results received from these models are on par with graphologists with an accuracy of 84%. This research work deals with the prediction of VIA Character classification of character strengths and virtues from handwriting images using a deep learning model. Correctly giving character strength score from handwriting image is the primary objective. This is the first known study that utilizes handwriting images for the prediction of VIA character score. Volunteers’ handwriting images will be gathered. Their VIA character score will be gathered from the VIA site. These data will be fed to a neural model for training. The trained model will be evaluated for the accuracy of the character score. After training the model for 360 epochs model gives us a loss of 23.029 overall for training, the lowest mean absolute error of 0.5800 is achieved by the forgiveness character, and the lowest mean absolute percentage error of 14.5788 is achieved by the forgiveness character.
Immersive Virtual Reality as a Tool for Education

After introducing the topic of education in immersive virtual reality (iVR), the authors describe the methodology and procedure used to test an educational game in virtual reality. The objective of this chapter is to contribute to the definition of a format for the evaluation of educational experiences in VR by describing the methodology adopted in the mentioned case study. A group of 30 students completed a lesson in virtual reality, and their experience was evaluated through qualitative (questionnaires, thinking aloud, interviews) and quantitative (task completion and time) tools. The results show some need for improvement of the simulation, but subjects were immersed in the experience and scored highly on the final assessment on understanding the educational content.
COMEX: A Multi-task Benchmark for Knowledge-grounded COnversational Media EXploration

Open-domain conversational interaction with news, podcasts, and other types of heterogeneous content remains an open challenge. Interactive agents must support information access in a way that is fair, impartial, and true to the content and knowledge discussed. To facilitate this, systems building on interactive retrieval from knowledge-grounded media are a controllable and known base for experimentation. A conversational media agent should retrieve relevant content, understand key concepts in the content through grounding to a knowledge base, and enable exploration by offering to discuss a topic further or progress to describe related topics. In this work, we release a new multi-task benchmark on COnversational Media EXploration (COMEX) to measure knowledge-grounded conversational content exploration. It consists of a heterogeneous semantically annotated media corpus and topic-specific data for 1) entity Wikification and salience, 2) conversational content ranking on heterogeneous media content, 3) background link ranking, and 4) background linking explanation. We develop COMEX with judgments and conversational interactions developed in partnership with professional editorial staff from the BBC. We study the behavior of state-of-the-art systems, with the results demonstrating significant headroom on all tasks.