Reviewerly/exHarmony huggingface dataset:
Hybrid-Aware Attention Network Incorporating Knowledge Graph for Recommendation

In recent years, knowledge graphs have been widely applied in the research of recommendation algorithms. However, most recommendation models focus on modeling item features in the relational space of the knowledge graph while neglecting the differences between the user side and the item side during feature extraction. Furthermore, they fail to comprehensively capture and fully utilize the high-order interaction relationships between users and items. To address these issues, we propose a method that combines knowledge graph and graph convolutional network, named the Hybrid-Aware Attention Network Incorporating Knowledge Graph for Recommendation (HAN). HAN jointly extracts the interaction information between users and items and the associative information of the knowledge graph to enable accurate recommendations. To validate the feasibility and effectiveness of the proposed model, experiments were conducted on three real datasets, demonstrating that the proposed algorithm achieves favorable recommendation performance compared to other benchmark models.
Differential Privacy Trajectory Data Publishing Method Based on RNN

User trajectory data is critical for developing location services applications and urban traffic management. However, user trajectory data contains sensitive information such as home and workplace, and the direct release of user trajectory data will raise serious privacy concerns. To solve this problem, differential privacy is an effective method to synthesize trajectory data. However, the existing trajectory publishing model only considers the relationship between the front and back trajectory points, and lacks flexibility in trajectory data extraction. Therefore, this paper proposes a new privacy publishing method for trajectory data, which combines the parameter minimum description algorithm with Dijkstra's method to select the characteristic trajectory points representing the entire trajectory. This method further improves the flexibility of trajectory selection, and the relationship between any two trajectory points is more closely linked by recurrent neural network. Experimental results show that our solution improves the availability of published trajectory datasets while protecting user privacy.
A comparative study for lung, colon and breast cancer diagnosis using different convolutional neural networks

In modern times, the prevalence of diseases, particularly cancer, is rising rapidly. Diagnostic errors pose a critical safety concern in healthcare due to the complexity of medical decision-making, disease variability, and human cognitive limitations. This paper aims to compare different convolutional neural networks (CNNs) to find the most accurate model that can classify lung, colon, and breast cancer using histopathological images. A convolutional neural network is a subset of machine learning. It is one of the various types of artificial neural networks which are used for different applications and data types. A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. We created a baseline model that consists of a basic CNN architecture. It serves as a starting point for comparison with more complicated models. The baseline model's performance is then compared to the performance of various pretrained models (ResNet-50, ResNet-101, VGG-16, VGG-19, Inception V3, and DenseNet-121). The dataset used consists of 25,000 histopathological images of both the lung and colon and 277,000 histopathological of breast images. Results show that the ResNet-50 model is the most efficient among all the other models, scoring a validation accuracy of 98.3%. Additionally, ResNet-101, VGG-16, VGG-19, and DenseNet-121 also demonstrated strong performance, scoring 95.8%, 96.6%, 95.1%, and 92.4%, respectively. Meanwhile, Inception-V3displayed a fair result, scoring a validation accuracy of 73.2%.
What Is the Metaverse?

Abstract This chapter discusses the development of virtual reality. It will then further discuss the contours and technical details of the Metaverse. The chapter will conclude by offering a brief reflection on the future of the Metaverse.
A Comparative Analysis of Machine Learning Algorithms for Accurate Step Detection in Wrist Worn Devices

This paper presents a comprehensive study on the application and evaluation of machine learning algorithms for accurate step detection on wearables, with a particular focus on smart wristbands. A unique data collection system was developed using an inertial sensor and an ESP-8266 kit to acquire both raw and processed accelerometer and gyroscope data. The collected data was transmitted via the MQTT protocol and then processed and labeled to indicate whether the step was taken or not. Five different machine learning algorithms such as AdaBoost, decision tree, k nearest neighbors, Naive Bayes, and random forest were used for step detection. In addition, 5-fold cross validation was used to ensure the reliability of the machine learning algorithms. The performance of each model was evaluated using the metrics accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve, area under the ROC curve. Comparative analysis revealed that machine learning algorithms can significantly improve step detection accuracy in wearable devices. The findings contribute to the advancement of health monitoring, fitness tracking and rehabilitation applications. Future studies aim to collect and analyze data with more sensor variations across a broader demographic.
Contrastive Learning for Reducing False Negatives with Global and local views in Augmented Data

Self-supervised learning is a technique that learns representations from large scaled unlabeled data. Among various learning algorithms in self-supervised learning, contrastive learning is a method that compares the similarity of dataset to learn their features and relationships. It has been widely studied and explored. However, contrastive learning currently faces the issue of false negatives. This false negative problem refers to the misjudgment of data, hindering the model’s learning process. Therefore, in this paper, research was conducted to select false negatives and learn in the right way using the global and local views and cosine similarity of data, and it was confirmed that the performance was improved compared to the existing contrastive learning model.
Implementation and Optimization of LeNet-5 Model for Handwritten Digits Recognition on FPGAs using Brevitas and FINN

Handwritten digit recognition using deep learning models is an active area of research with wide-ranging applications. However, the high computational requirements of these models limit their deployment on resource-constrained devices. To address this challenge, we propose an optimized implementation of the LeNet-5 model for handwritten digit recognition on FPGAs using the Brevitas and FINN frameworks. We trained the model on the MNIST dataset and quantized it to reduce its memory footprint and improve inference performance. We then used FINN to generate hardware operators and RTL code, which were synthesized for an EFINIX FPGA. Additionally, we used a RISC-V processor and custom HLS VDMAs to interface with the FPGA and enable image loading and result display through GUI. Our approach achieved a classification accuracy of 96.64% on the test set, while consuming significantly lower power than CPU and GPU implementations. Our methodology demonstrates the potential of leveraging FPGA hardware acceleration to enable efficient deployment of deep learning models on resource-constrained devices.
Incorporating Text Embeddings in Deep Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction plays a vital role in enhancing the effectiveness of online advertising and recommendation systems. Existing deep CTR models primarily focused on numerical and categorical features, emphasizing their significance in accurately forecasting user engagement. However, the textual information associated with the content, such as product descriptions, movie titles, and ad titles, can provide valuable insights into user preferences and interests. In this paper, we explore the effects of incorporating text features into deep neural network-based CTR model using text embeddings. We employ various pre-trained word embeddings, such as Word2Vec, Globe, and FastText, to convert the textual information into dense vector representations that capture semantic meaning and contextual relationships. Our findings reveal that the incorporation of text embeddings significantly boosts the model’s ability to capture fine-grained textual information, leading to more accurate CTR predictions.
Sentiment Analysis for Patient Reviews in Hospitals by CNN and LSTM Neural Networks Using Pretrained Word Embeddings

Medical reviews of patients are very important for the medical management departments and sentiment analysis is one of the most popular application areas of Natural Language Processing. In this study, we use and compare different neural architectures for sentiment analysis of patient reviews about hospitals. We developed four neural models to classify the patient review as positive or negative. First, the data retrieved from an online platform were preprocessed. Then, before the neural training, Skipgram word embeddings were carried out for transfer learning. Finally, training was performed. A model which we trained has only fully connected dense layers. One of the trained models includes LSTM and fully connected layers. One of them includes CNN and fully connected layers. One model has CNN, LSTM and fully connected layers. After the training phases our best two neural models (LSTM-CNN and LSTM) have achieved sentiment classification with over 85% performance.
Time-minimum motion handling of open liquid-filled objects using sparse sequential quadratic programming

The paper presents an efficient approach to minimize motion time of an industrial robot so that it can successfully manipulate an open and liquid-filled object in pick-and-place operations. It is first proposed a motion planning optimization problem, where the total motion duration is considered as a cost function. Moreover, the robot physical limits such as its joint positions, velocities and accelerations are used as the optimization constrains. On the other hand, to ensure an open and liquidfilled object always upright, orientation constraints of the robot end-effector are taken into account. More specifically, roll and pitch of the end-effector are proposed to be fixed during the transportation, which ensures there is no tipping over in the object. The formulated motion planning optimization problem is then efficiently solved by using the sparse sequential quadratic programming method. Our approach excels in optimizing the motion trajectory by leveraging its flexibility, accommodating various trajectory shapes that satisfy the kinematic conditions. The optimization leads to more efficient and effective motion execution, resulting in a substantial reduction in the overall motion profile duration. Extensive evaluation of the proposed approach on a KUKA robot model demonstrates its effectiveness.
Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM

Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recent years. In this study, LSTM and pre-trained T5, Pegasus, BART, and BART-Large model performances were evaluated on the open source dataset (Xsum, CNN/Daily Mail, Amazon Fine Food Review, and News Summary) and the prepared resume dataset. This resume dataset consists of many information such as language, education, experience, personal information, skills, and this data includes 75 resumes. The primary objective of this research was to summarize resume text. Various techniques such as LSTM, pre-trained models, and fine-tuned models were assessed using a dataset of resumes. The BART-Large model fine-tuned with the custom resume dataset gave the best performance.
Two-level Alignment-based Unsupervised Domain Adaptation for Semantic Segmentation of Remote Sensing Images

Semantic segmentation is an essential analysis task for understanding remote sensing images. Recently, many supervised semantic segmentation models have achieved high performance. However, this performance tends to decline when there is a distribution shift between the source and target domains, such as a change in the geographical area or sensor mode. One solution to overcome this issue is to use unsupervised domain adaptation, which transfers the grasp of a model trained on a source domain with accessible labels to the target data domain without label access. This paper proposes a new unsupervised domain adaptation method for remote sensing images. The proposed approach leverages a combination of Fourier transform-based image-to-image translation to diminish the shift in the input-level space and the fine-grained domain discriminator to address the shift in the class-based feature-level space. The experimental results demonstrate that our proposed method effectively improves the performance of cross-domain remote sensing semantic segmentation tasks.
Detecting Code Smell with a Deep Learning System

Code smell detection is one of the most significant issues in the software industry. Metric-based static code analysis tools are used to detect undesirable coding practices known as code smells and guide refactoring requirements. Furthermore, the usage of deep learning-based techniques has emerged in code analysis tasks. The line and block level detection capability of metric-based tools provides an advantage over deep learning system systems. This study aims to develop a deep learning-based system for inter-procedural code smell detection supporting line and block of code. We created an experimental dataset by gathering code from GitHub repositories and detecting code smell on these codes using the metric-based SonarQube tool. Recurrent neural networks and transformers implementations of deep neural networks were applied to detect code smells. We also employed cosine similarity and k-Nearest Neighbor machine learning techniques for a comprehensive comparison. The proposed system achieves an average accuracy of approximately 80%. These findings indicate that the proposed system can help software teams in identifying potential interprocedural code smells.
Detection of Turkish Fraudulent Domain Names to Proactively Prevent Phishing Attacks Using A Character-Level Convolutional Neural Network

Phishing attacks, which aim to steal sensitive information by tricking users into fraudulent websites, continue to pose significant financial and operational threats to organizations and individuals. While various studies have focused on detecting phishing websites using different techniques, only a limited number of studies have addressed the proactive detection of attacks through the analysis of domain names. This paper introduces a novel and proactive approach for real-time detection of phishing websites solely based on their domain names by leveraging the characteristics of Turkish domain names. Detecting the legitimacy of a website based solely on its domain name presents a significant challenge due to the limited availability of features that can expose phishing attacks. To address this problem, the paper proposes the use of character-level embeddings and a convolutional neural network (CNN) to develop a specialized classifier. A new dataset consisting solely of Turkish domain names and their corresponding labels is constructed for training and evaluating the CNN model. The experimental results demonstrate that our proposed model achieves an accuracy of 90%, which is highly promising given the intricacies of the problem at hand. Our study contributes to the advancement of proactive phishing detection methods and underscores the significance of considering linguistic and regional factors in phishing attack analysis.
Transforming RTLS Data Architecture to Big Data Architecture for Location Data

Real-time Location System (RTLS) locates and monitors any person or asset within a defined zone covered by a radio frequency (RF) network. This system produces location data for each entity to track from several times per second to every few minutes, depending on how often it is desired to refresh the location data. The increase in the volume of data that is required to be collected, stored, processed, analyzed, and reported, is one of the reasons for the development of big data technologies. This paper aims to explain the transformation of data architecture according to the target short-term and long-term analytical needs of RTLS and to compare traditional architecture and big data architecture on read times and storage costs. In order to achieve that, the data in the relational database system is extracted to.csv files in its raw form with a microservice triggered daily within the relational database management system. Data is processed in Python language to meet the three different reporting requirements of RTLS. Analyzed data is stored in a column-family NoSQL database Apache Cassandra by modeling differently in line with the query parameters for faster query purposes. By not archiving the location data on the relational database, without affecting the performance health of RTLS, meaningful results are produced from this data and are reported on the application. As the size of the input and output data used for reporting increases, the need to use a NoSQL database is essential in terms of query response times. NoSQL database systems may require the same data to reside in multiple tables, so the storage cost per row is about 67% lower than SQL database, but as the number of queries to the data increases, storage costs can exceed SQL databases.
Turkish Sentiment Analysis: a Comparative Study on Different Sentiment Dictionaries with Generated Features and Presenting a New Sentiment Dictionary

Sentiment analysis is a research area that aims to find out people’s opinions by matching data to topics, notions, etc. There are several approaches for sentiment analysis (e.g., machine learning-based, dictionary-based, hybrid-based, etc.). In this study, we presented a new tripolar Turkish sentiment dictionary, SentiMenTR, which consists of bigrams and unigrams. To compare the performances of SentiMenTR and other Turkish sentiment dictionaries (SWNetTR++ and SentiTurkNet), we conducted experiments on two Turkish datasets containing documents labeled as negative or positive. For experiments, firstly, we vectorized the documents by features extracted using polarity scores belonging to dictionaries. Afterward, we fitted machine learning models with these features. According to the experiment results, SentiMenTR performed better than other dictionaries. We aim to extend our dictionary, develop a negation handler module, and conduct more comprehensive experiments with deep learning methods in the future.
The Effect of Document Length on Machine Learning Success in Text-Based Data

Natural Language Processing (NLP) is an important research area for artificial intelligence studies. In the process of processing textual data, feature extraction and the creation of the word-document vector are very important. Especially for machine learning algorithms, these numerical vectors play a critical role in the creation of the model. Textual data must be preprocessed to generate these vectors. There are common methods such as removing stopwords, converting text to lowercase, and cleaning punctuation marks. The effects of these methods on the created model have also been investigated in the literature. However, it has not been investigated how the length values of the text can affect the model created. So how does a document or text having less than 10 or 20 characters affect the machine learning model? This study was carried out in order to solve this problem and fill the gap in the literature. The effect of text length on text classification models has been tested with different feature extraction methods.
A New Exact Inversion Approach for Takagi-Sugeno Fuzzy Systems Representing SISO Nonlinear Systems

Inversion of Takagi-Sugeno (TS) fuzzy systems has significant applications in nonlinear control systems. In this study, a new approach is presented for the exact inversion of TS fuzzy systems representing affine-in-control Single-Input-Single-Output (SISO) nonlinear systems. The proposed approach provides exact inverse solutions for TS fuzzy systems whose rule consequents are in the form of linear state-space models. Although this type of fuzzy system is widely used in control applications to represent SISO nonlinear dynamical systems, there is currently no existing inversion method in the literature for a fuzzy system with a similar structure. In the proposed method, the inverse model is obtained by using a rule-by-rule inversion approach. In order to show the exactness of the proposed inversion approach, a simulation example is given for a TS fuzzy model of a single link manipulator. Simulation results validate the exactness of the proposed inversion method.
An Intelligent Movies Recommendation System Based Facial Attributes Using Machine Learning

Movie theaters and platforms offer a wide selection of movies that require filtering to match the preferences of individual users. Recommender systems are an effective tool for this task. This study introduces a hybrid recommender system that combines collaborative filtering and content-based approaches to provide personalized movie recommendations. The proposed system considers age, gender, emotion, and genre attributes to ensure the suitability of the recommended movies. The proposed system targets the visitors of cinema theaters to try a new experience of choosing the next movie to watch by recognizing the visitor's face to determine his/her age, gender and emotion using camera. Therefore, our system is expected to achieve better results than traditional approaches. The system's performance is evaluated using standard metrics such as precision, recall, and F1-measure. The findings indicated that the proposed system outperforms the benchmark system in the most tested scenarios. However, the precision of the benchmark work is slightly higher in (8–14 and 15–24) age groups.
Comparison of Multiple Regression Particle Swarm Optimization Algorithms and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload / Under-Loaded Detection in Cloud Datacenter

Power consumption of the data centers in recent times is a big challenge presently. Virtualization plays a key role in improving efficacy. Virtual Machine consolidation (VMC) is a technique for efficient usage of a resource to reduce energy consumption by consolidating Virtual Machines (VMs) into fewer physical machines without Service level agreement violation (SLAV). In this paper, we compare two algorithms called Multiple Regression Particle Swarm Optimization (MRPSO) Algorithm and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization (MR-MOSLO) Algorithm for host overloaded and under loaded detection. The comparison result shows that the performance of MRPSO is better to MR-MOSLO when the number of VMs is few.