Reviewerly/exHarmony huggingface dataset:
TransUnet for psoriasis lesion segmentation

Outstanding performance of the transformer-based model in the field of natural language processing has piqued the interest of researchers in investigating these techniques for computer vision. And the most popular UNet model is considered a major player in the field of image segmentation. Thus, in this paper, we have proposed the transformer-based UNet model for the complex task of psoriasis lesion segmentation from raw color images. One of the major challenges for our segmentation task is the scarcity of datasets and to overcome this challenge we have exploited the EfficientNetB1 transfer learned model as a backbone for our segmentation model. The proposed model is evaluated for the 70:30 hold-out data division technique and the segmentation performance is evaluated using the Dice Score (DS) and Jaccard Index (JI). The value of DS and JI obtained for the intended task are 0.9571 and 0.9201 respectively with the proposed model. Comparative analysis with different derivatives of the UNet model and state-of-the-art literary work shows the better performance of our proposed model.
Blockchain for Challenges of Logistics and Supply Chain Information System

The globalization of Logistics and Supply Chain has escalated the demand for efficiency in its Information Systems and Security. With supply chain systems now crossing the boundaries, it has become increasingly important to manage the vast amount of data it generates. Conventional supply chain system has a lot of manual tasks to be done which is time consuming and makes it prone to more errors. Supply chain systems may leverage blockchain technology (BT) to streamline supply chain operations in a trustworthy and secure way. The integration of the blockchain into supply chain systems looks promising but has a lot of challenges right now. The challenges are primarily because of the relatively new technology with lack of standards and guidelines, risk of adoption, and other risks like cost, time and information sharing. This paper investigates the challenges of logistics and supply chain and its solutions using BT followed by analysis of their pros and cons.
Deep-3DConvNet: A Network to Detect Abnormal Activities at Megastores

These days, there has been a rapid increase in cases of abnormal human behavior at megastores/shops, where people commit theft by stealing, consuming, or unwrapping packets when no one is seeing and then leaving the place without paying. Such unusual actions cause huge losses in business. Therefore, there is an urgent need to attract the research community's attention to detect abnormal events at megastores. To address this issue, we have designed an advanced three-dimensional convolutional neural architecture to identify abnormal activities at megastores. The proposed network is 15 layers deep, takes a video stream of resolution 120× 120 as input, and produces classification results as output. It extracts fine-tuned as well as general details from the video feed using small and large-sized 3D convolutional filters and categorizes them into respective classes. The proposed architecture is trained and tested on a synthesized action dataset that consists of human actions distributed into five classes: normal, stealing, eating, drinking, and damaging acts. Experimental results show that our model outperforms other state-of-the-art approaches with an accuracy of 88.88%.
Multi-Part Knowledge Distillation for the Efficient Classification of Colorectal Cancer Histology Images

Colorectal cancer is the most common type of cancer after breast cancer in women and third in men after lungs and prostrate cancer. The disease rank third in incidence and second in terms of mortality, hence early diagnosis is necessary for the correct line of treatment. Knowledge distillation based models boost the performance of small neural network and are performing efficiently for various image classification based tasks. In this work, a novel knowledge distillation based technique is developed to efficiently classify colorectal cancer histology images. Unlike traditional distillation, out method performs distillation in parts. Instead of supervising the student with a converged knowledge of teacher, the proposed method is fetching the teacher's knowledge at regular intervals and providing these knowledge to the student model during student training process. Through this multi-part distillation technique student can effectively learn the intermediate representational knowledge rather than the abstract knowledge of the teacher and hence boost the overall performance of the model. The the proposed model has achived 92.10% accuracy.
Forensic Analysis of Windows 11 Prefetch Artifact

The Operating System creates numerous objects to improve its efficiency and user experience and such objects are called artifacts. These artifacts record crucial data about the user activity. Such artifacts are the start point of any investigation as they can be an additional lead to a forensic triage. Prefetch file is one among various objects, presence of which confirms the execution of a particular application. Prefetch gives additional inside for the purpose of investigation. Thus, this paper brings out the forensic value of it, the tools required to decode the information it contains and also look in various caveats in interpreting this artifact to learn its strength and weaknesses to properly incorporate in support of opinion derived by the analyst. In this work, Prefetch is forensically examined to bring out its forensic value, knowledge it contains and all of that in whole or in parts can be used to help advance in investigation. Paper also brings out the difference in format of this artifact among various version of Windows OS.
Advancement in Communication using Natural Language based VideoBot System

Natural Language Processing is a subset of Artificial Intelligence, which focuses more on the natural language communication and speech recognition. After evaluation of AI and its sub branches, the automated answering system or now it's called as chatbot is a very popular and widely used application. One of the limitations of this application is that it is text based. This application is not so effective when we want to develop a dynamic system. In this paper, authors have proposed a VideoBot application, which is a more effective way of communication while interacting with end users. While simple chatbots, which don't have any emotional attachment with end users, as compared to this Videobot have more effectively connected with end-users, as it has videos with emotional expressions.
Voice Based Authentication Using Mel-Frequency Cepstral Coefficients and Gaussian Mixture Model

Voice operated devices are becoming popular nowadays. For this it is necessary that voice authentication is secure. In this paper, we address some known attacks like replay, personification and attacks using AI voice bots and limitations like text and language dependency of human voice authentication systems. We have also developed an interactive system to tackle these problems. The system verifies the user by performing voice matching as well as on an intellectual level by asking questions which only humans are able to answer and not any AI bot. In the system, an average user requires around 35 seconds for registration and around 25 seconds for authentication. The system's accuracy comes out to be 97.8% for English speakers and 95% for Hindi speakers.
Realtime Deepfake Detection using Video Vision Transformer

Practically, Deepfake technology has given people access to generate fake videos that look like real content using neural networks, and can further create misconceptions and deceit about the innocuous elements of society. This technology can prove fatal not only to national security but on an international level. Existing methodologies that apply deep learning to automatically extract salient and discriminative features to detect Deepfakes based on typical CNN-LSTM models tend to have their shortcomings. Having said that, we propose a system that extracts Spatio-Temporal features and achieves Real-Time Deepfake detection using Transformers. For the end user, a web application was developed, which with utmost simplicity allows the uploading of a video that will be further authenticated within the application and, at the same time, features the authentication of live meetings.
A Novel Method in Procedural Maze Generation

The concept of procedural content generation (PCG) in game development has existed for a long time. It is used in games for the generation of unique content which help in making the game re-playable. Procedural content generation can be used in almost all game design areas. From level generation to creating a storyline for the game, the use of PCG helps in decreasing the overall time required to design an interesting game. The only problem with PCG is that it is hard to implement and optimize. This document consists of an algorithm that works on a type of recursion and the concept of snappable meshes. This is done using prefabs and other features that are available in Unity Engine[6]. All the methods mentioned in this document are done using Unity Engine. Unity Engine is one of many famous game engines that are available online. The algorithm mentioned helps in creating a procedural maze. The game levels are generated dynamically, allowing the player to experience new levels and avoid repetition of the same levels as in traditional games. The algorithm and its implementation in Unity Engine are explained in detail. How the meshes are spawned and placed dynamically to generate a level is also discussed.
Explainable Approach for Species Identification using LIME

Plant identification has a wide array of applications in the fields of agronomy and the discovery of natural and medicinal products. This research aims to explore various deep learning techniques like InceptionV3, Xpection, and ResNet to identify plants. Highly accurate machine learning models generally lack explainability and interpretability. Neural networks are usually opaque systems and thus a direct understanding of the interpretations becomes necessary. We aim to remove this ambiguity of how the model reaches its conclusion by introducing Explainable AI (XAI) techniques. Explainability aims to break such barriers by diminishing the lack of transparency in Artificial Intelligence and Machine Learning models, thus taking a step toward making AI reliable. In this paper, Convolutional Neural Network has been used to identify Vietnamese medicinal plant images based on the characteristics of the leaves, stems and other parts of the plant. Upon identification, our paper also elaborates on how each model predicts which part of the image helps the CNN model to make a prediction by integrating Explainable AI (XAI) using the Lime package. Through this research, we generated images using LIME package which highlight pixels that determine the result of our plant identification process.
Sentiment Classification Twitter of LRT, MRT, and Transjakarta Transportation using Support Vector Machine

The use of public transportation facilities such as MRT, LRT, and Transjakarta by the people of the capital city is an alternative in reducing congestion. However, the services provided by MRT, LRT and Transjakarta transportation service providers vary, such as positive and negative responses. The effectiveness of public transportation facilities can be seen through public opinion. This study aims to classify positive and negative tweet sentiments sourced from Twitter data using the Support Vector Machine (SVM) algorithm. The results of this study indicate that the Support Vector Machine method is able to classify positive and negative sentiment text with an accuracy result of 91.89% with 79.2% positive sentiment and 20.8% negative sentiment.
Comparison of K-means and K-medoids in Tourist Attraction Clustering based on Visitor Characteristics

The purpose of this study was to determine the clustering of tourist objects scattered in the Bangkalan area. Clustering has been done using data on the characteristics of tourist visitors. This approach is used to see how much influence the characteristics of visitors have on the attractiveness of tourist objects. We apply methods in data mining, especially algorithms for partitioning methods, namely K-Means and KMedoids. K-Means and K-Medoids were implemented in finding clusters of tourist attractions on the characteristics of gender, age, occupation, education, and marital status. The clustering results show that the K-Means and K-Medoids methods have their respective advantages on the characteristics of visitors. The best accuracy results are 90.48%, which is obtained on gender characteristics with the K-Medoids method
An approach to detect abusive content incorporating Word2Vec and Multilayer Perceptron

With the rapid growth of social media text, millions of negative comments are flowing on social webs and social networking sites. Abusive content is harmful to people and societies that can provoke various criminal offenses like hate crimes. Hate speech is also a form of abusive content. An automatic and improved detection system for hate speech can help to reduce this problem. Implicit abusive content requires contextual semantic and syntactical analysis. We propose a novel abusive text detection model with the word2vec model and compositional vector model to analyze text more semantically and syntactically. The proposed model considers the English language dataset for abusive text. The abusive content detection model exhibits achievable performance compare to various deep learning and machine learning classifiers. Among all models, Multilayer Perceptron classifier achieves 86% accuracy compared to other models.
Evaluation of Information Security Management Using the KAMI Index Framework

The KAMI index is an instrument to analyze the quality of the completeness and maturity of the implementation of information security for an organization based on the criteria of SNI ISO/IEC 27001. The KAMI index is not intended to analyze the feasibility of the existing security model, but as a tool to describe the readiness of the current information security framework. This research will conduct a self-assessment using the KAMI Index with the Data Center object at the Samarinda City Communication and Information Center in 2020. The agency has not yet received an overview of the readiness condition of the Data Center information security framework. This study uses a qualitative descriptive method approach. The results of the recapitulation of the KAMI index self-assessment value for Data Centers is 88 with details: information security governance is 42, information security risk management is 0, the information security framework is 2, information and technology asset management is 22 and information security is 22. The score for the category The electronic system in the Data Center is 26. Based on the final evaluation result of the self-assessment of the KAMI Index for the Data Center at Diskominfo Samarinda City, the readiness status is: “Not Eligible” with the category of ”High” electronic system so there is too big a difference in the value of management information security management and Electronic Systems (SE) category scores.
Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

In this analysis, the methods used are the K-Nearest Neighbor classification method and the Logistic Regression classification method with data taken on the twitter application. This study examines the level of accuracy in public sentiment regarding covid-19 vaccination with positive and negative labels. The AUC value in the KNN algorithm with TextBlob labeling is 0.765 with and 0.76S for VaderSentiment labeling are both included in the fair classification criteria. Meanwhile, the Logistic Regression algorithm produces an accuracy of 84.97% with a ratio of 90:10 for Labeling TextBlob, while for Labeling VaderSentiment with a ratio of 90:10 results in an accuracy of 85.22%. Both algorithms are validated using K-Fold Cross Validation with a fold count of 10. The comparison results obtained when conducting an evaluation with the confusion matrix showed that the Logistic Regression algorithm with VaderSentiment labeling had the highest accuracy value compared to the K-Nearest Neighbor algorithm with TextBlob and VaderSentiment labeling.
Webots Simulator for Lyapunov-based Cooperative Omnidirectional Mobile Robots Evaluation

A multi-robot system is a set of robots that share a common objective and collaborate to achieve it. Multi-robot systems can address distributed and complicated real-world issues more effectively in various industries, including logistics, transportation, and industrial manufacturing. The higher system performance because of the collaborative efforts of numerous robots provides a substantial possible benefit of using a multirobot system rather than a single robot. The Lyapunov control approach is one advanced approach to coordinating multiple robots in warehouse logistics applications. The method has been successfully simulated on point masses. However, to be physically implemented in robotic devices still requires several processes that are not simple. The case study focuses on finding the possibility of a distributed cooperative mobile robot using a LiDAR sensor to recognize the ‘friend’ robot and the obstacle. This paper shows that each robot can recognize well between its neighbor or obstacles and maintain its formation of 1 m during the trip with obstacle presence. The simulations were performed in Webots to verify the proposed algorithms. The mathematical analysis and the experiment prove the system's stability by seeing the robots' velocity.
Effective methods for solving systems of nonlinear equations of the algebra of logic based on disjunctions of complex conjunctions

In order to simplify logical statements and reduce the time for solving systems of non-linear Boolean equations, a criterion for the absorption of complex conjunctions by a first-order neighborhood of conjunctions of statements of a separate class of systems of non-linear Boolean equations above the second degree, given by Zhegalkin polynomials, is proposed. In the class of systems of nonlinear Boolean equations under study, the logical formulas of Zhegalkin polynomials are completely or partially divided into some linear factors. As a result, logical formulas are reduced to the disjunction of complex elementary conjunctions, consisting of the product of individual arguments, linear polynomials or their negations, on the basis of which a system of nonlinear Boolean equations is obtained. Some problems of minimizing special disjunctive normal forms obtained from the Zhegalkin polynomial above the second degree of special classes are considered.
The Geographic Smoothing Potential of Solar Irradiation in Java, Madura, and Bali Electric Power System

Indonesia is making ambitious efforts to increase the usage of renewable energy sources to meet the country's energy needs. However, there were still concerns about how the intermittent nature of solar power output might affect the grid. This study focused on the problem of intermittent solar power and examined the potential role that distributed geographic location could play in lowering the overall intermittency. 18 solar irradiation stations scattered throughout the Java, Madura, and Bali (Jamali) electric power systems were used as the irradiation data source. The total irradiation of all locations was then evaluated for its intermittency using Power Spectral Density (PSD), Welch Method. According to this study, connecting 18 stations reduces intermittency by 70 to 93 percent, depending on the timestep. The study also demonstrates that after 10 locations, the reduction becomes almost insignificant.
Sentiment Analysis of The Public Perspective Electric Cars in Indonesia Using Support Vector Machine Algorithm

Transformation of energy use to switch to green energy consumption is urgently needed. Pollution levels and residual combustion yields have increased in each period. Air pollution is one of the contributors that plays a significant role in environmental air quality. Transportation accounts for about 27% of the air pollution generated from combustion gases. Governments in various countries. The main objective of this study is to find out how public sentiment towards electric car vehicles is through data sourced from data sets through the social media platform twitter and by using the analysis of the sentiment support vector machine approach as a data playing algorithm. The processed data set amounted to 1502 Tweet opinions with a positive sentiment classification distribution of 79%, negative sentiment of 21% with an accumulated accuracy rate of 87%. In addition, this study also visualizes the results of the analysis in the form of a value graph so that it can help electric vehicle industry players to understand public sentiment better and accurately and help data information to encourage the Indonesian government to increase the use of green energy that supports a green and sustainable environment.
Minimalistic Vector Extension for Image Detection on Edge Nodes Using ‘VGG16’

As Technological advancements are crossing the boundaries like never before, postulating the need for smarter algorithms such as NN’s and faster hardware to process them becomes evident in this era. The hardware available in the open market are general purpose, limiting the complete usability of hardware for the intended CNN application. This paper addresses the implementation of a custom 32bit vector extension for a complex neural network called VGG16. We have implemented a highly parallelised vector core that takes care of computationally intensive mathematical operations. Using four lanes, each lane with nine vector registers with three vector elements in each register. Vector changing techniques was used to optimise resource utilisation. An improvement of 30% was achieved with respect to throughput compared to existing designs which can process the VGG16 neural network model. As VGG16 is an inefficient model in the industry, the current design projects the improved performance of VGG16 using the vector core. This enables the implementation of image detections on edge IoT applications.
Bogus Specie Reccurence

The quantity of fake money notes being produced and replicated on a global scale has increased considerably as colour printing technology has advanced. Producing used to be defined to print stores, but now anyone with a affordable price laser printer can produce a specie paper with pin point accuracy. As a result, bogus notes are constantly being usedin place of legal users.. woefully, nation have been troubled for damages as illegal and dark specie. Money notes that are counterfeit are likewise a major problem. Leads to the improvement of a technology which can detect bogus currency papermore quickly and efficiently. XGBOOST structure is a way to authenticating nations banknotes. Tocheck monetary notes, image processing concepts are applied. The properties of Indian currency notes are extracted in this article. The attributes of the paper are removed using MATLAB software. The suggested system's advantages include ease of use and high performance speed.. The voice will be presented in regional languages, and the outcome will indicate whether the money note is real or not.. It requires phases as grayscale reformation, edge detection, segmentation, and so on, carried out using suitable techniques.