Reviewerly/exHarmony huggingface dataset:
Enhancing Security in Automobile Edge Computing through Federated Learning and Blockchain

Vehicular networks that consist of interlinked automobiles and transportation network are prone to cyberattacks due to the increased use of software and the presence of wireless interfaces. To counter these threats, intrusion detection systems can be tailored efficiently. In recent years, there has been significant development in detection of malicious attack traffic through the use of machine learning. However, traditional systems require powerful computing devices to continuously train and update complex network models, which can reduce the efficiency and effectiveness of the system due to resource constraints, untimely updates and dealing with large volumes of data. To address this issue, a cooperative intrusion detection mechanism which had been proposed has been improved with a classification based novel approach, that eases the burden by distributing the training model to distributed edge devices such as connected vehicles and roadside units. The previous approach has a slow response time to the increasing network issues. The proposed work offers a new approach through federated learning with the Extra Trees Classification algorithm. The Extra Trees Classifier performs better than other machine learning models in all aspects. The proposed approach provides a faster response to security threats in automobile edge computing. The use of a distributed federated approach reduces the load on the server while maintaining confidentiality by aggregating models using a blockchain.
Management and Marketing Assistance Program at CV Sweetroom

A management system is a collection or combination of processes that start from planning, organizing, implementing, monitoring and evaluating, and work together to achieve certain goals in an efficient and effective way. Sweetroom does not yet have a management system, so it becomes an obstacle in implementing work coordination and organizational communication. The goal of a management system is to obtain something of better value by exerting effort. At Sweetroom the Standard Operating Procedure (SOP) for each work unit is not yet available, so it is necessary to prepare SOPs for work activities in each work unit. The results obtained from preparing the SOP are increased employee discipline and increased sales in Sweetroom.
Simulation of distributed denial of service attack against ethereum smart contract on the blockchain

The perception and functional infrastructure of smart networks are changing as a result of the advent of blockchain technology and the proliferation of IoT-driven devices. Deploying Ethereum&apos;s smart contracts, the blockchain allows secure administration, authentication, and access to IoT equipment. Blockchain-IoT systems are creating a high amount of sensitive data due to increased demand and growth. DDoS assaults pose the most serious danger to smart contracts in blockchain-IoT systems, according to a new report. Smart contracts in blockchain-IoT systems currently lack any kind of security mechanism. Artificial intelligence approaches for threat categorization utilizing Adaptive Weighted MLP ("Multi-Layer Perceptron") are used first in the proposed security architecture to provide complete autonomy in making decisions. The primary job of the transaction handling engine is to use adaptive weighted MLP to categorize transactions into regular and attack kinds. If the transaction is legitimate, the smart contract will carry out its business logic and add it to the blockchain network in the cloud, where it will be included to future transactions. SHA-256 hash value calculation is used to generate the certificate key. Ethereum Blockchain-based proof of authority is used for data aggregation and transaction addition in the Consensus blockchain mechanism. Homomorphic cryptography schemes are also applied to enhance security. To avoid intrusions, the security of the system is improved. The proposed framework&apos;s performance is measured against a real-world IoT dataset, called BoT-IoT20,21, which comprises current DoS ("Denial-of-Service"), DDoS (Distributed Denial-of-Service Attack"), and other IoT-based attack samples. We used a variety of assessment measures to determine how effective the suggested distributed system is in finding DDoS attacks. It&apos;s also been likened to some of the most modern approaches in the field.
Image forgery detection using deep neural networks: current state-of-the-art and future directions

Digital image forgery pertains to the malevolent alteration of digital images with the intent to deceive or misguide observers. It has become an increasingly prevalent issue in today&apos;s digital age. Digital image forgery is carried out using multiple image tampering techniques as well as post-processing methods. This paper aims to provide a comprehensive survey of the various techniques employed in digital image forgery. We explore the underlying principles and algorithms behind these techniques, highlighting their commonalities and differences. Additionally, we discuss the emerging trends in forgery detection and authentication methods, emphasizing the need for robust and efficient solutions to combat the ever-evolving landscape of digital image forgery. Through the consolidation of contemporary research within this domain, this paper aims to furnish scholars with a valuable reference for comprehending the most recent advancements in the realm of digital image forgery detection, while also illuminating prospective avenues for further research.
Image enhancement by cascading of filters for noise reduction

With numerous applications in the fields of medicine, defence, space exploration, security systems, and surveillance, digital image processing is a widely researched field. Image acquisition and transmission is the first step of image processing applications which result into a image frequently degrades due to causes such noise, temperature, dust and malfunctioning charge coupling devices in electronics circuits. These factors produce noisy images by changing some or all of the image&apos;s pixels. These noises fall into several categories including Gaussian noise, Shot noise, Spike noise, and speckle noise. Noise removal as a pre-processing step helps to enhance image quality. Medical images corrupted by different noises may cause disturbances in AI based automated diagnosis. The dataset used in this work is of medical images. In this paper, cascade combination of filter techniques has been proposed for noise removal. The results show improvement in the image quality compared to the individual or single filtering with reference to different quality metrics. The algorithm has been simulated in MATLAB.
Automated Scoring of English Essays in CEFR Levels using LSTM and DistilBERT Embeddings

The Common European Framework of Reference for Languages or often abbreviated as CEFR is an international standard that is currently used globally to measure language fluency. One of the abilities that makes up one's language fluency is the writing skill which is measured through a written exam. This research is aiming to build an automatic essay scoring system in CEFR level utilizing machine learning models. The research is done by developing two machine learning models, namely a LSTM based model and another where LSTM and DistilBERT is combined in one pipeline. The training was done with practice texts data from the EFCAMDAT open-source corpus my EF English First and University of Cambridge. Before training, hyperparameter tuning is done to obtain the best hyperparameters for each model and it is done using the help of the Optuna framework. Training is then carried out using the obtained hyperparameters and model performance are measured using accuracy and F1-measure at each epoch. After training is finished, the models were tested, and the final performance which contains accuracy, F1-measure, and confusion matrix for each model were obtained. Additionally, classification report consisting of precision and recall for each class prediction results made by each model was obtained after testing. Final testing results showed dan the model that combined produced the best results in predicting English essay scores in CEFR levels.
Sexism Detection and Classification Using RoBERTa and Data Augmentation

Sexism has been growing in social media due to the lack of consequences one would get for doing it. Explainable Detection of Online Sexism is a dataset provided by Rewire Online to develop models that can detect sexism and explain it by further classifying sexist texts into categories. Using provided dataset, we build models to detect and classify sexism into categories and vectors (sub-categories) using transformer-based model, RoBERTa. As the dataset provided contained a significant imbalance between the classes, the impact of different data augmentation method is also experimented to find out which method will give the best result. The experiment was done by using three different methods: random oversampling, easy data augmentation, and backtranslations. As a result, EDA gave the best performance for sexism detection, back-translations gave the best performance for category classification, and random oversampling gave the best performance for vector classification.
Automatic Knowledge Graph Construction over Efficient Information Extraction Networks

Knowledge graph construction(KGC) aims at constructing a knowledge graph from enormous amounts of unstructured sources. Once unstructured information is stored in a graph-structured format, it becomes easier to access and utilize. In this paper, we propose a automatic knowledge graph construction method based on an efficient contextual model, which is composed of layered convolutional neural networks(CNN) and attention-based bidirectional LSTM layers. The model achieves similar performance compared to the RoBERTa(pretrained model) but is time conserving and is lighter than this kind of pre-trained model. In addition, we propose a framework to enrich the constructed knowledge graph(KG) with external high-quality sources using relation linking. The refined KG is visualized based on Neo4j. Our experiments highlighted that our method achieved a few points of amelioration that can be readily applied to other similar domain fields. Knowledge graph database holds the potential to contribute to the advancement of educational initiatives in the field of music e ducation, thereby enhancing educational prospects.
Message from the General Chairs TransAI 2023

Artificial Intelligence (AI) is concerned with computing technologies that allow machines to see, hear, talk, think, learn, and solve problems even above the level of human beings. On the one hand it allows data to be analyzed by real-time models that enable unprecedented levels of accuracy and efficiency. On the other hand it enables domain specific problem solving and knowledge discovery that cannot be easily done by humans.
Automatic Clustering by Geometric Method of Tangent Lines with Points

Clustering is the division of data samples into separate groups based on standard features to discover their structural properties. In recent studies, researchers introduced the peak clustering algorithm, which combines density-based and center-based clustering. However, the algorithm has two main weaknesses. (1) Insufficient accuracy in determining cluster centers in low-density clusters (2) Recognizing actual data points as noise data points. Researchers presented the Extreme clustering algorithm as a more efficient solution that overcomes the weaknesses of the Peak Clustering algorithm while still benefiting from its advantages. However, the Extreme clustering algorithm is not very accurate in separating the boundaries of overlapping clusters. We have developed an automatic clustering solution based on the geometric method of tangent lines with points. Our method uses the Extreme clustering algorithm to determine the Extreme points. Then, it applies the Apollonius problem and its definition of polygons to cover all the points until it reaches the exact clustering boundary. After evaluating the results, the proposed clustering algorithm in this paper can increase the accuracy of separating the boundaries between different clusters.
Generation of Test Cases for Identification of Crime against Women through HLR and VLR

In recent years, it is observed that crimes against women are increasing exponentially growth manner and the nature of the crime may be sexual harassment at any place including the workplace, acid attack at any place, rape, domestic violence, and many more which the criminal may leave his location treated as visitor location for performing criminal activities against the women whose mobile is treated as a home location. In the present work, a criminal scene is established through the mathematical technique for matching these two locations for identification of the crime. From the literature, it is observed that mathematical matching techniques are not in use for the identification of crime. The purpose of the present work is to fill this gap by developing a Unified Model by the use of Unified Modeling Language which has been validated through valid test cases after implementing the model in the Python programming language.
Cybersecurity Cloud-Based Online Learning: A Literature Review Approach

Cloud-based online learning is the electronic learning activity that supports teaching and learning (T&amp;L) that could be done from anywhere and in the world. Some of its benefits are scalability and affordability that could in a decision-making support on the mechanisms of material selection. Cloud computing has been adopted by most universities around the world. In this regard, lecturers and students will use it to facilitate T&amp;L; however, due to concerns of information technology or systems security, cloud-based online learning users are also not immune. In this regard, the users could be affected by different cybersecurity attacks. In this paper, a systematic literature review method was used to sift the different models and solutions used to address the cybersecurity concerns surrounding cloud-based online learning. A brief Likert-scale questionnaire was used to obtained data that could corroborate the systematic literature findings. In this regard, a group of 20 online learning designers were sampled as participants. It was found that the confidentiality, integrity, and availability issues are a concern. This led to issues of security awareness, authentication and blended attacks being issues. In this regard, a cloud-based online learning model is not immune from security issues. In this paper, a conceptual framework as the line-of-defense is proposed as a solution towards having a cybersecure cloud based online learning.
Backslash map: An Automated Vulnerability Scanner

Web application vulnerability detection is critical to cybersecurity. Web applications are vulnerable to attacks from a variety of sources, with varying degrees of scale and complexity. Therefore, it becomes essential to employ an automated tool that can effectively examine and identify vulnerabilities within the application. This research paper introduces Backslash Map, an automated system for penetration testing that enables efficient identification of web application vulnerabilities. The proposed system utilizes signature-based scanning techniques and predefined payloads. It offers key features such as subdomain enumeration (passive or active), port scanning, and detection of SQL injection, server-side template injection, and code execution vulnerabilities. During the vulnerability assessment, Backslash Map inspects and filters requests to identify vulnerable endpoints while performing subdomain enumeration on domain services. The experimental results prove the promising performance of the proposed system when compared with the state-of-the-art.
Perancangan Mesin Pengupas Kulit Kentang Dengan Metode VDI 2221

ABSTRACT&#x0D; Designing a product means describing the ideas you have to solve a problem. By using the VDI 2221 method to design a product, there are at least 3 points that are included in the works that are made realistic, aesthetic, and ergonomic. In the manufacture of potato peeler machine aims to help business people with a simple way of working and low production costs. then the purpose of designing this potato peeler machine is to make an innovation of a potato peeler that is practical, energy efficient and inexpensive, to know the process of peeling the machine, to know the components and functions of the tool. With the VDI 2221 design method as the method used to solve problems and optimize the use of raw materials and production processes. In this method there are several stages of design between wish list, concept design, design of form and detailed design. The potato peeler machine is designed using solidwork for 3D. The step of making a potato peeler starts from determining the design of the tool, determining the main components, the process of designing the process of making a potato peeler tool.&#x0D; Keywords: Potato Peeler Machine, VDI 2221 Method
Performance Bounds for Policy-Based Reinforcement Learning Methods in Zero-Sum Markov Games with Linear Function Approximation

Until recently, efficient policy iteration algorithms for zero-sum Markov games that converge were unknown. Therefore, model-based RL algorithms for such problems could not use policy iteration in the planning modules of the algorithms. In an earlier paper, we showed that a convergent policy iteration algorithm can be obtained by using a commonly used technique in RL called lookahead. However, the algorithm could be applied to the function approximation setting only in the special case of linear MDPs (Markov Decision Processes). In this paper, we obtain performance bounds for policy-based RL algorithms for general settings, including one where policy evaluation is performed using noisy samples of (state, action, reward) triplets from a single sample path of a given policy.
Bayesian Estimation for Linear Systems with Nonlinear Output and General Noise Distribution Using Fourier Basis Functions

Bayesian estimation can be used to estimate the state of dynamical systems, but its applicability is hampered due to the curse of dimensionality. This paper aims to mitigate this bottleneck for a relevant class of systems consisting of a linear plant with bounded input, driven by stochastic disturbances with non-linear noisy output; the distributions of the disturbances and noise have a bounded support but are otherwise general. Using a frequency-domain interpretation of the operations of the Bayes' filter, we show that, under mild assumptions, exact Bayesian estimation can be pursued in a countable space of Fourier series coefficients, rather than in the usual functional space of probability densities. This fact leads to a natural approximate method, where the Fourier series coefficients corresponding to high frequencies are discarded. For this approximate method, the complexity of the conditioned state distribution, measured by the number of Fourier coefficients, remains constant at prediction steps and grows only linearly at each update step. The applicability of the results is illustrated in the context of electron microscopy, where a residual error analysis indicates that the approximation is accurate.
Quantum Optimal Control for the Shaping of Single Photons

The transmission of flying qubits carried by itinerant photons is fundamental in quantum communication networks. To physically match the receiver system, the single photons must be prepared in proper shapes, and this leads to a variety of flying-qubit control problems. In this paper, we introduce the optimal control theory to the shaping of single photons, where the control to be optimized are coherent driving fields. We design gradient-based algorithms to minimize the shape difference between the emitted and desired single photons. Simulation results show that the optimization can achieve high-fidelity in the generation of decaying photon shapes with a two-level atom. However, its performance is limited and hence has to be combined with incoherence controls when the target shape has a rising part.
Improving the timeliness of college English teaching model based on multilevel fuzzy evaluation

Abstract In order to meet the changing educational needs, this paper collects the factors related to the teaching mode through the method of multilevel fuzzy evaluation and establishes a multilevel fuzzy evaluation system including the factors of teaching methods, textbook selection, learning resources and so on. To obtain quantitative satisfaction results, fuzzy numbers are employed to calculate the satisfaction evaluation of teaching programs. Establish a judgment matrix using triangular fuzzy numbers and hierarchical analysis to assign weights to different indicators. The evaluation results of each factor are synthesized to obtain the evaluation results for the timeliness of the English teaching method. The results show that although there is not much difference in the basic level of English between the experimental class and the control class, the experimental class, after using the method of this paper, has a measure of qualitative data higher than the standardized metric of 71.195, and the academic grade reaches a full score of 10. It is further verified that by using the method based on multilevel fuzzy evaluation, universities can optimize their teaching mode in a more targeted way to adapt to the increasingly changing educational environment.
SenticGAT: Sentiment Knowledge Enhanced Graph Attention Network for Multi-view Feature Representation in Aspect-based Sentiment Analysis

Currently, computational intelligence methods, especially artificial neural networks, are increasingly applied to many scenarios. We mainly attempt to explore the task of fine-grained sentiment classification of review data through computational intelligence methods, especially artificial neural networks, and this task is also known as aspect-based sentiment analysis (ABSA). We propose a new technique called SenticGAT which is a multi-view features fusion model enhanced by an external sentiment database. We encode the external sentiment information into the syntactic dependency tree to obtain an enhanced graph with rich sentiment representation. Then we obtain multi-view features including semantics, syntactic, and sentiment features through GAT based on the enhanced graph by external knowledge. We also design a new strategy for fusing multi-view features using the feature parallel frame and convolution method. Eventually, the sentiment polarity of a specific aspect is determined based on the completely fused multi-view features. Experimental results on four public benchmark datasets demonstrate that our method is effective and sound. And it performs superiorly to existing approaches in fusion multiple-view features.