Reviewerly/exHarmony huggingface dataset:
A Secured Novel Classifier for Effective Classification Using Gradient Boosting Algorithms

This research provides an anomaly detection model based on Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Decision Tree (DT) methodology of classification. The RSA algorithm is used to improve the privacy and security of data in a cloud setting. To ensure security, the RSA algorithm is employed for the key generation process. Feature selection is critical for effective classification. To choose the best features, the Recursive Feature Elimination (RFE) technique is used. To improve the speed and precision of the arrangement and relapse issues, the angle boosting calculations have been utilized. These calculations incredibly improve the exhibition of the interaction in light of their attributes like fast preparation and decrease overfitting. It has been demonstrated in numerous applications particularly in Antimoney laundering in cryptographic forms of money by applying in their framework as well as in their record and exchange levels. The exhibition of angle boosting calculations has been tried with the standard existing datasets. To assess the proposed strategy, we led probes of the NSL-KDD dataset. The outcomes show that the LightGBM algorithm has a delightful presentation over the other two methods as far as the parameters accuracy, precision, and recall followed by XGBoost with the histogram strategy.
Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm

Border security is a process which measures the border management ideas by a country or set of countries to wage against unspecific and unauthorized travel or trade across the country borders, to bound non-legal deport, various crimes combat, and foreclose dangerous criminals from entering in the country. A system will help in keeping a check on those personnel’s who forge with the legal document with an intension to cross the border. This article discusses about border security of whole Indian context, and there are various such systems which have been built since 2010 as wireless sensor network system named “Panchendriya”. Remote and instruction manual switch mode arm system using ultrasonic sensor for security of border. This article we have made use of Haar-Cascadian along with LBPH algorithm with their functioning. The result and discussion section we compare most recent face recognition techniques that have been used in the last ten years. The proposed prototype is discussed and shown through simulation model, it provide better result compare to existing model. The proposed Haar-Cascade and LBPH algorithm provide 10% better performance.
Methods for Optimal Feature Selection for Sentiment Analysis

Recently, the research on sentiment analysis has raised enormously. In sentiment analysis, the opinions of the user are analyzed. The opinions given by the user on any product, movie, place, or food are generally in text form which is then eradicated to identify the positive, neutral, and negative opinions. Customer reviews provide a significant amount of data each day, thus the features that can be drawn from this data are very valuable. An efficient feature selection method is required which generated features that can be separated from each other and has minimum co-relation. In this paper, we have used four feature selection methods, namely forward selection wrapper method, decision tree method, chi-square method, and ANOVA method for selecting the optimal features from the given data. Logistic regression, support vector machine, random forest, Naive Bayes, K-nearest neighbors, and k-means classifiers are examined to see how feature selection approaches affect their performance.
Software Fault Prediction Using Particle Swarm Optimization and Random Forest

Software fault prediction deals with the identification of software faults in the early phases of the software development cycle. It is essential to detect the defects in software as early as possible for the smooth functioning of the software and to reduce the resources and time taken for its maintenance in the future. It can be done either manually, or by using automatic predictors. As the complexity of the software increases, it becomes hard to identify the faults manually. So, to deal with the faults in a timely and more accurate manner, there are many automatic predictors already in use, and various new ones are also being proposed by several researchers. In this study, a method to improve the fault prediction rate in software fault prediction is proposed by combining particle swarm optimization (PSO) with the random forest (RF) classifier. NASA MDP Datasets, which are considered large-scale datasets, are utilized to test the proposed model. The findings show that PSO with RF classifier increases performance when applied on the NASA MDP Datasets and overcomes prior research constraints.
Abstractive Summarization is Improved by Learning Via Semantic Similarity

Semantic similarity is a training approach that takes into account the semantic meanings of the generated summaries. The most difficult to find is sentence similarity. The sentence is determined not only by the tokens themselves, but also by how two sentences are interconnected and framed. There are multiple degrees to semantic similarity, and sentences might be similar in one condition but opposite in another. This can be accomplished through pre-training and fine-tuning phases of the BERT. The presence of multiple potentially valid predictions is one of the challenges of abstractive summarization. Alternative replies are ineffectively handled by mostly used non-subjective functions for supervised learning. Instead, they serve as a pre-training sound. In this research, we are proposing a semantic similarity technique for training that takes into account the semantic meanings of summaries. Identification semantic similarity score among generated and reference summaries is our training goal, which is computing by adding additional layer. Our proposed architecture obtains model achieves a new state-of-the-art action, with a ROUGE-L score of 41.5 on the CNN/DM dataset, by using pre-trained language models. We also conducted human evaluations to supplement the computerized validation and achieve higher scores than the baseline and reference summaries.
Experimenting Different Classification Techniques to Identify Unknown Author Using Natural Language Processing

Every author has its unique style of writing and also a certain fashion of using certain words making their literature different from others. In today’s scenario, where there is a lot of plagiarism and also exploitation of various articles it becomes important to identify the author. This experiment aims at detecting authors based on their use of certain words and their writing style. An accuracy of 84% in detecting the actual authors was observed. Behavior of various mathematical models was also observed for predicting the authors. Impact of stop words and cleaning of sentences in predicting the result was also analyzed.
IoT Assistant for People with Visual Impairment in Edge Computing

We advocate that technology can make it possible to develop devices capable of recognizing people and objects, mainly with the aid of machine learning, computer vision, and cloud computing. Such devices can be used in the daily life of a visually impaired person, providing valuable information for guiding their steps, providing a better quality of life. This paper proposes an architecture that uses computer vision and applies deep learning techniques to an Internet of Things (IoT) assistant for people with visual impairment. Considering that an IoT device is a limited device, it’s used edge computing to improve the proposed architecture so that the device may be updated over time. The recognized object is converted into Text To Speech (TTS), allowing the user to listen to what has been recognized and also the distance from the user to the object. Unrecognized objects are sent to the cloud, and the device receives a re-trained network. The proposed architecture has been implemented using known and proved technologies such as Raspberry Pi 3, USB camera, Ultrasonic Sensor module, You Only Look Once (YOLO) algorithm, Google-TTS, and Python. Experimental results demonstrate that our architecture is feasible and promising.
A Proposal to Trace and Maintain Requirements Constraints of Real-time Embedded Systems

The development of Real-Time Embedded Systems (RTES) considering critical and volatile system and software requirements is a difficult and error-prone activity. The capability to describe system components while highlighting and maintaining their correlations at different abstraction levels and refinements has a significant impact on RTES development. Most techniques for designing RTES present many problems and limitations regarding tracing RTES constraints along of architectural design. Moreover, there is a gap in integrated strategies to evaluate the correctness of these constraints from model specification to the system models realization. This article aims to perform a study on Model-Driven Systems Engineering approaches applied in the design and traceability of specific RTES constraints. The main objective of this study is to automatically perform an evaluation of traced non-functional concerns and provide feedback to the developers regarding the validity of the simulated constraints. In order to achieve this objective, this study, initially, develops a formalized manner to specify RTES constraints in architectural viewpoints and a tool to evaluate the real simulated values of these specifications.
Improving Learning Activities and Gross Motor Skills Blended Learning with the Home Visit Method

This research is a type of research (action research), aimed at collecting data on whether blended learning with the home visit method can increase learning activities and gross motor skills of group B. The formulation of the problems in this study is: 1) whether blended learning with the method home visit can improve children's gross motor skills? 2) Can blended learning with the home visit method increase children's learning activities? 3) How is the blended learning process learning with the home visit method can improve gross motor skills and learning activities of children in group B? Based on the findings of action research (action research), 1) the researchers drew the conclusion that blended learning with the home visit method can increase learning activities and gross motor skills of group B students. Students' gross motor skills in cycle 1 by 25%, cycle 2 by 30%, and in cycle 3 it increases to 75%. 2) while in student learning activities cycle 1 by 25%, cycle 2 by 40%, and in cycle 3 it increases to 80%. The blended learning process with the home visit method that has been implemented properly and proven to be the right alternative can improve the ability of children's learning activities and gross motor skills in group B. The effectiveness of blended learning can be seen from the increase in children's learning activities in each cycle and gross motor skills.
Investigation on e-Commerce Platforms for Tackling e-Business Security Challenge

Securing e-Business from threats and risks has become an urgent necessity nowadays especially with the growth of the cyber-crimes. Enterprises need to provide securely services and gaining the trust of the customers is the bedrock toward achieving any enterprise’s objectives and goals. Otherwise, making e-Commerce platforms exposed to the cyber-crimes will affect the e-Business performance and reputation and may destroy the e-Business at all. In this paper, the different e-Commerce security challenges are discussed by presenting the most important vulnerabilities related to the different components of the e-Commerce including the user, the security techniques, the Information Technology environment, and the e-Commerce platform. Then, the best practices that enterprises should follow to ensure a safer e-Business environment are provided. Moreover, different attack detection mechanisms deployed in e-Commerce platforms have been presented. Finally, investigations are conducted on some e-Commerce platforms in order to show how the e-Business still suffers from information disclosure.
Auto-reversing intelligent obstacle avoidance system based on optical fibre sensor

Aiming at the problems of low obstacle avoidance accuracy and long obstacle avoidance time in the traditional obstacle avoidance system, an automatic reversing intelligent obstacle avoidance system based on optical fibre sensor is designed. The system hardware consists of optical fibre sensor module, power module, microprocessor, display output module and voice alarm module. The wavelet packet analysis method is used to extract the obstacle features in the process of automatic reversing. According to the feature extraction results, the optical fibre sensor is used to measure the distance, calculate the spatial position of the obstacle features in the process of automatic reversing, so as to realise automatic reversing and intelligent obstacle avoidance. The simulation results show that the maximum obstacle avoidance deviation of the designed system is 0.8 mm, which is much smaller than that of traditional obstacle avoidance systems; the obstacle avoidance response time of the proposed system is only 18 s, which is also much smaller than other systems. The proposed method exhibits higher obstacle avoidance accuracy, shorter obstacle avoidance response time, and excellent application performance.
Towards 2030: future-proofing human capital management

Purpose This paper is designed to highlight the role that HR can play in helping to shape the future of work in a world where digitisation, artificial intelligence, machine learning and robotics are increasingly the norm. This paper aims to help HR professionals to understand how foresight thinking can be used to connect weak signals that are surfacing in the present with future potential outcomes. In this way, incoming disruptions can be anticipated and addressed. Design/methodology/approach An anticipatory HR can plan for what is next. The authors analyse trends such as dematerialized work and explore the emergence of key challenges and requirements that HR will have to face. To address these issues, the authors have created methodologies, derived from the world of defence and intelligence as well as curated and crowd-sourced scenarios from the world of work. An extensive bibliography of evidence and research supports our observations and recommendations. Findings Based on research and analysis undertaken for our book, HR Futures 2030 – A Design for Future-Ready Human Resources published by Routledge, this paper presents ten traits of the future, a collection of characteristics that are highly relevant for the HR function. This paper concludes with five recommended focus areas for 2023 to help ensure that businesses stay ahead of the curve. These have been selected from 22 further characteristics explored in the book. Originality/value This paper offers a unique and thought-provoking perspective on how HR professionals can help their organisations to think beyond the current paradigm and re-imagine the world of work. How to build systemic readiness and align skills and work processes for the future.
Increasing Digital Literacy Through Video Based Learning in Balam Jaya Kampar Village

Watching is one of the daily activities carried out by all levels of society, young and old to children. In the digital era, many children do not understand what they are watching and receive harmful information. The fact is that watching activities provide many benefits, such as getting information anywhere and anytime easily. The purpose of this activity is to provide education to students through digital literacy that there is a lot of learning that can be taken from watching activities and developing writing, understanding, and listening skills. The method used in this activity is Video Based Learning which is the delivery of learning through video by showing films. The results showed that the percentage level of video eligibility was 75% with good qualifications. Based on the results of the study, it can be concluded that the Video Based Learning method is feasible to use in the learning process
What Behavioral Signals could be Used to Identify Users?

The current feasible solutions of behavioral biometrics analysis could identify or verify the user during login or his work inside a secure system. But there are many new approaches that could be used in the future to detect the unauthorized use of electronic devices. What about using not only computers but also cell phones, virtual reality, and augmented reality solutions? How the identity of the users could be monitored during his secured session? How have modern machine-learning approaches changed the game? Many new ideas and state of art concepts will be presented in this talk.
Stereo Matching with Supplementary Boundary Information

Stereo matching, which estimates the disparity of corresponding points in paired images, has problems with accuracy degraded by occlusion, repeated texture, texture-free areas, and gloss. To address this problem, we focused on object boundary information, which is less susceptible to such problems. This is because occlusion is more likely to occur at the boundaries, and even if the corresponding points disappear due to occlusion, the boundaries still have the corresponding parts. The boundary information is also robust to such problems because it is not affected by repeated textures, untextured areas, or gloss. Therefore, we propose a method for generating the boundary ground truth from the depth ground truth and develop a network and loss function for estimating the boundary information. We also propose that the accuracy can be improved by incorporating the estimated boundary information into a state-of-the-art network as an auxiliary.
CT Brain Image Synthesization from MRI Brain Images Using CycleGAN

Medical images from multiple modalities are essential for diagnosis and effective treatment of various diseases. However, obtaining these images separately is a time and cost consuming task for both patients and physicians. Whereas CT scanning also increases the radiation exposure for patients. Therefore, generating CT scans from radiation-free MR images is a very desirable task. Synthesized brain CT scans are useful for obtaining cranial information that is difficult to obtain from MR imaging but gives crucial information for brain surgery and treatment. In this study, we propose a deep learning-based method to synthesize CT image from MR image. We present a cycle GAN-based method to synthesize CT brain image from MR brain image. We also propose a novel normalization method called as range-of-interest (ROI) normalization to emphasize the tissue and bone regions. Using this method, the synthesized CT image can be treated in the same way as the actual CT image. Additionally, we evaluate the impact of intensity normalization using three different intensity normalizations for synthesizing MR-CT images, which is an important image preprocessing step. Our proposed ROI normalization for CT images and max-min normalization for MR images generate the highest-quality synthetic image results, with Mean Absolute Error at approximately 94.60 HU of the produced 3D volume of CT images.
Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation

This paper studies a practical domain adaptive (DA) semantic segmentation problem where only pseudo-labeled target data is accessible through a black-box model. Due to the domain gap and label shift between two domains, pseudo-labeled target data contains mixed closed-set and open-set label noises. In this paper, we propose a simplex noise transition matrix (SimT) to model the mixed noise distributions in DA semantic segmentation, and leverage SimT to handle <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open-set label noise</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> and enable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">novel target recognition</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> . When handling open-set noises, we formulate the problem as estimation of SimT. By exploiting computational geometry analysis and properties of segmentation, we design four complementary regularizers, i.e., volume regularization, anchor guidance, convex guarantee, and semantic constraint, to approximate the true SimT. Specifically, volume regularization minimizes the volume of simplex formed by rows of the non-square SimT, ensuring outputs of model to fit into the ground truth label distribution. To compensate for the lack of open-set knowledge, anchor guidance, convex guarantee, and semantic constraint are devised to enable the modeling of open-set noise distribution. The estimated SimT is utilized to correct noise issues in pseudo labels and promote the generalization ability of segmentation model on target domain data. In the task of novel target recognition, we first propose closed-to-open label correction (C2OLC) to explicitly derive the supervision signal for open-set classes by exploiting the estimated SimT, and then advance a semantic relation (SR) loss that harnesses the inter-class relation to facilitate the open-set class sample recognition in target domain. Extensive experimental results demonstrate that the proposed SimT can be flexibly plugged into existing DA methods to boost both closed-set and open-set class performance.
Public Communication in Transformation: Algorithmic Culture

This textbook offers an interdisciplinary, comprehensive and state-of-the-art overview of the media linguistics approaches to explain and understand digital communication and multimodality. Linking the fields of communication studies, applied linguistics and journalism, it grounds communication practices in a deep understanding of the social and societal implications of language use in digital media. The tools to analyse multimodal texts are analysed in light of the advantages and constraints that different communication modes pose, both individually and in combination. Aimed at upper level undergraduates and graduates in applied linguistics, communication and media studies, including journalism and PR, this textbook contains case studies and professional examples highlighting the interplay between language use and digital communication and encouraging the reader to reflect on the themes covered, and put the acquired knowledge into practice. Online resources for students include videos, writing techniques, a guide to multimodal texts analysis, additional case studies and a glossary.