Reviewerly/exHarmony huggingface dataset:
A Blockchain Based Data Auction Mechanism

In the present digital era, data has become an increasingly valuable asset to both enterprises and society. Despite the vast amount of available data, the absence of a reliable means of transaction often hinders the direct benefits that data can bring. This results in a lack of willingness among data owners to conduct transactions, poor data mobility, and an inability to fully realize the value of data. In order to promote the transaction and circulation of data, this study proposes a data auction model based on blockchain technology, which ensures transparency, security, and efficiency in data auctions. Furthermore, an optimal auction algorithm is devised, which demonstrates that in the process of data trading with multiple participants, the auction model can maximize the utility of data owners, improve their willingness to trade, enhance data liquidity, fully realize the value of data, and provide a platform for sustainable development of data trading.
Application of WG-CNN Neural Network Approach Based on Early Flood Detection using Satellite Images

Natural disasters, such as hurricanes and earthquakes, often cause flooding in underdeveloped countries, leading to devastating loss of life and property. Providing communities with advance notice of an impending flood helps solve this problem by allowing residents time to evacuate and secure their belongings. However, the variety of early warning system solutions introduces a tangle of conflicting requirements, such as cost and reliability, and creates a number of intriguing problems from factors as varied as technological, social, and political. This problem is rarely met in affluent countries, let alone developing ones, because of the complexity of these systems and the requirement for autonomy within the setting of a developing country, all while staying maintainable and accessible by nontechnical employees. Following an explanation of the issue at hand, the paper moves on to a discussion of a potential solution, some preliminary experiments with the solution, and some lessons learned so far. Preprocessing, Segmentation, Feature Extraction, and Model Training are the four essential components of the suggested method. Preprocessing makes use of techniques such image scaling, rotation, shift scale, RGB shift, contrast, and normalization. For this purpose, K-Means is employed. Following feature extraction with measures like mean, entropy, standard deviation, and variance, WG-CNN is then used to train the model. When compared against two existing models, including CNN and SVM, the suggested model fares exceptionally well.
Developing Algorithms for Personalized Recommendations Based on User Behavior

To enhance the performance of user personalized recommendation algorithms, this study offers a user personalized recommendation calculation based on deep learning network. Efficient and accurate personalized recommendation algorithms can effectively increase user experience fulfillment. Considering user behavior, this research suggests a useful advice. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their considerations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their contemplations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. The methodology solves the sparsity problem and outperforms existing algorithms. Implementing a user behavior-based recommendation method, which gauges users' interests based on certain evaluations of item qualities, is the primary commitment. Furthermore, this strategy makes use of a sequential purchase pattern to raise the caliber of recommendations.
Parameter Fitting-Based Fast Imaging of Axisymmetric Flame Temperature Field Using Wavelength-Modulated Lateral Shearing Interferometry

A parameter fitting-based fast imaging method for axisymmetric flame temperature field was proposed in a lateral shearing interferometer. In the extinction state, a sawtooth waveform was used to modulate a distributed feedback (DFB) laser to obtain the quantitative mapping from modulated voltage to phase. In the combustion state, the laser was rapidly driven by an irregular waveform of five steps. Linear equations were established, and the phase difference caused by the flame at each pixel was extracted by parameter fitting. Flame phase distribution was obtained to image refractive indexes and temperatures. Compared with the existing waveform fitting-based wavelength modulation for flame monitoring, the proposed method reduced the modulation steps to five and improved the imaging speed up to eight times. In numerical simulations, an axisymmetric counterflow flame was reconstructed to evaluate the performance of the proposed method. Reconstruction errors of phase difference, refractive index and temperature distributions were 0.0117, 0.0692 and 0.0795, respectively. In experiments, three counterflow flames of different flow conditions were imaged. The flame structures were clearly visualized, and the interferometric temperatures matched well with the readings of a thermocouple. A Bunsen-type flame with 100-Hz acoustic excitation was monitored at 8 kHz. The variation of the temperature distributions well reflected the flame oscillations. The frequencies of fundamental, second and third harmonics were captured, and the harmonic at 99.43 Hz caused by the acoustic excitation was also precisely identified.
An Experimental Study of the Sample Complexity of Domain Adaptation

In this study, we experimentally investigate the sample complexity of semi supervised domain adaptation with deep neural networks. Sweeping the hyper-parameters of domain adaptation neural networks relying on the MMD distance measure and the number of training samples in a controlled manner, we study the test accuracy of the target samples. Both labeled and unlabeled samples from the source and the target domains are used as inputs to the neural network in the experiments. Our experimental findings suggest that the minimum number of samples required for guaranteeing a fixed experimental target accuracy level increases quadratically with both the number and the dimension of the MMD layers in the network used for aligning the source and the target domains. We observe that this relationship is in harmony with some well-known theoretical bounds in the classical deep learning literature. Meanwhile, we also investigate the optimal weighting strategy between the classification loss functions of the source and the target samples, concluding that increasing the weight of the target loss improves the performance as the number of target samples increases.
Context and Sentiment Aware Chatbot for Tourism Applications

Recently, advances in the informatics field have changed the tourism and hospitality sectors drastically. The interaction between the service provider and the customer is revolutionized by employing sophisticated chatbots. Although there are a lot of chatbots, most of them are not sentiment and contextaware. In this study, we developed a context and sentiment-aware chatbot for tourism applications. First, we created a database that includes frequently asked questions in the tourism area. We then automatically correct mistakes in the customer queries and compute the similarity of queries with existing sentences in the database and respond to them with the corresponding answer. We also monitor the sentiment of users continuously and try to estimate the context (before sales and after-sales). The proposed system has promising results for real-life applications.
Alignment of Image-Text and Video-Text Datasets

In this study, the alignment of video-text and imagetext datasets is studied. Firstly, similarities are calculated over the texts in the two data sets. A retrieval setup with visual similarities is then applied to the subset which is created via calculated text similarities. A BERT-based embedding vector method is applied to the raw and pure texts. As a visual feature, object-based and CLIP-based methods are used to define video frames. According to the results, alignment with CLIP features achieves the best results in the subset created by filtering using raw text.
SIU2023-NST - Hate Speech Detection Contest

Hate speech spread on social media can strongly affect people and societies, especially when it targets a specific group of people in terms of religion, culture, or a specific social situation, such as refugees. For this reason, the detection and elimination of hate speech in social networks have attracted the attention of natural language processing researchers in recent years. A competition is organized to benchmark progress in Turkish hate speech recognition, within SIU2023 Computational Social Sciences special session, with four different tasks. A total of 20 teams registered for the competition, while eight teams submitted results at the end. The details of the winning models and their results are explained in this paper.
Data Valuation Methods for Federated Learning

Modern distributed networks such as smartphones, wearable devices, and self-driving vehicles generate a wealth of data. As the computation, storage, and battery capabilities of these devices grow, local data storage and processing become easier and more secure. This has led to a growing interest in federated learning which provides training of deep learning models while keeping the training data decentralized. During the training, the performance of deep learning models can be improved by filtering redundant, malicious, and abnormal samples with data valuation methods. In this work, the aim is to improve federated learning models by integrating data valuation methods into the training process. For this, a two-layered convolutional neural network is trained on the MNIST dataset inside a small federated learning network. The Shapley and Leave-one-out based data evaluation methods developed in this study have resulted in an 8.81% accuracy improvement during experiments conducted on the MNIST dataset with corrupted labels.
A Privacy-Preserving Machine Learning Ensemble for Spam Detection

In this era of spam and fraudulent emails, it is important to detect spam to reduce the chances of falling victim to fraud. Many email applications such as Gmail, and Outlook run a spam detection algorithm to direct spam mail to the spam folder. However, tricksters learn to escape the spam box by studying the behavior of these standard algorithms. This research study presents a module that can be used to improve the performance of spam filters. This module can be integrated as an extension to these spam filters. The module accepts only encrypted emails to eliminate privacy concerns. Since no copies of the decrypted emails are maintained on the server, the emails are not available to the spam filter module. This feature is deemed important as privacy concerns are getting louder. The spam detector uses multiple machine-learning algorithms including a bagging classifier. It also features a voting classifier to finalize the classification of mail into ham or spam. The ensemble is tested with two datasets and a procedure to build a framework for constructing best-performing models has been discussed.
Enhanced Phishing URL Detection Using Leveraging BERT with Additional URL Feature Extraction

Threats, including phishing, are still a widespread and growing problem in today's digital society because fraudsters use increasingly complex techniques to deceive users and gain illegal access to sensitive information, effective phishing detection systems are essential for protecting individuals and enterprises. In this study, we provide a novel method for phishing detection that takes advantage of the capabilities of the BERT model and URL feature extraction. In testing employing a dataset of 200,000 URLs, including original and phishing URLs, our technique achieved a remarkable accuracy of 97.32 percent. By properly preparing the information and using the BERT tokenizer to tokenize the URLs, the contextual understanding was made possible. Additionally, the model's ability to discern between legitimate URLs and phishing efforts was enhanced by the capturing of structural features using URL feature extraction techniques. The high degree of accuracy attained by our technique shows that it is useful in phishing attack detection and prevention, offering a strong defense against fraudulent online activity and boosting user security.
The Alliance of HE and TEE to Enhance their Performance and Security

While protection of data at-rest and data in-transit can be achieved using standard algorithms and technologies, the protection of data in-use is still, to a large extent, an open issue. Homomorphic Encryption (HE) and Trusted Execution Environment (TEE) are among the most popular approaches to shield computations. The former ensures high security guarantees but it suffers from a significant overhead. The latter, instead, provides lower execution time but it is affected by security drawbacks. In this paper, we propose SOTERIA, a privacy-preserving computation solution that combines HE and TEE to mitigate their limitations. The approach foresees the execution of sensitive processing with homomorphic encryption and the usage of a TEE to perform switches between different homomorphic cryptosystems. In fact, there are different kinds of computation algorithms where the co-existence of linear and non-linear functions makes the HE-based processing even more onerous. SOTERIA is developed in the context of the ENCRYPT project and will be validated in a use case of financial data processing.
An Improved GIFT Lightweight Encryption Algorithm to Protect Medical Data In IoT

The Internet of Things (IoT) enables the interconnection of devices that collect massive amounts of data, making IoT security requirements crucial and secured through encryption. However, traditional encryption protocols are no longer suitable for all IoT scenarios. We propose a new lightweight encryption method optimized for patient information protection in healthcare, considering the limited capacity of portable medical devices. Our method has been experimentally demonstrated to be effective, with a largest entropy of 7.9917 in medical images (computed tomography) and average coding and decoding times of 3.8952 sec and 3.0584 sec, respectively. The encoded image exhibits an even distribution of pixels and lower correlation coefficients between neighboring pixels, supporting the effectiveness of our less complex method compared to current state-of-the-art methods.
A Machine Learning Approach for Predicting Lung Metastases and Three-Month Prognostic Factors in Hepatocellular Carcinoma Patients Using SEER Data

Using SEER data, this work seeks to create a machine learning (ML) model to predict lung metastases (LM) and three-month prognostic variables in hepatocellular carcinoma (HCC) patients. The study comprised 34,861 HCC patients, 1,783 (5.11 %) of whom had lung metastases, and 859 were suitable for the 3-month prognostic model. The ML models were cross-validated twice, and with an AUC of 1 and an F1 score of 0.997, the random forest (RF) classifier was found to be the best choice for predicting LM. The Easy Ensemble (EE) classifier was utilized to address the dataset's class imbalance problem. The study also indicated that employing resampling approaches such as SMOTE can result in synthetic data, which can reduce model reliability, making EE the recommended choice for addressing the class imbalance. Overall, this study aims to improve clinical decision-making by providing a comprehensive predictive model for HCC patients with LM and a 3-month prognosis.
Improving Microwave Component Characterization Using Wavelet Denoising Method

In this study, an efficient approach is presented to improve the characterization of various microwave components commonly used in communication and radar applications, such as antennas and power dividers. The components were initially simulated and then fabricated using the Computer Simulation Technology (CST) software. Vector Network Analyzer (VNA) measurements of the fabricated components were performed using a low-cost but noisy coaxial cable, and the measurement results were processed using a wavelet-based noise reduction method. For comparison purposes, the Haar and Daubechies- 4 (DB4) wavelet functions were applied separately, and the results were examined. It was observed that the correlation and root mean square error between the ideal and measurement results improved in a positive direction with the noise reduction application. This approach provides significant cost and labor advantages, particularly when expensive elements such as gold and silver are used in coaxial cables that are physically free from noise. The experimental and numerical results show good agreement between the ideal simulation results and the filtered measurement results.
Modeling Camera ISP Pipeline with Deep Learning

Today, images presented to the users by digital cameras are created by sequentially processing the raw data received from the sensor by different signal processing modules. In this traditional approach, in which the signal processing modules are designed independently of each other in accordance with the purpose, the final image quality might be adversely affected due to distortion effects occurred at modules and carried throughout the camera pipeline. In order to eliminate these effects and to ensure the optimization of the camera bus from beginning to end, it is seen that researches on modeling the camera pipeline with deep network architectures have increased in recent years. Within the scope of this paper, the images in the Zürich dataset created for this purpose were edited using Topaz Gigapixel AI and Photomatix Pro 5.0 software, and the final model performance was examined by retraining the CNN (Convolutional Neural Network) based PYNET[1] network architecture, which is one of the pioneering studies in this field. Considering the PIQUE (Perception based Image Quality Evaluator) metric, which does not need a reference image, the performance of the created model is increased; at the same time, it has been seen that HDR tonemapped image can be constructed by revealing the details in the low and high exposure regions.
Multitask YOLO: Versatile Perception Network for Autonomous Driving

Autonomous driving requires perception systems that use computer vision for object detection and segmentation, but these tasks require significant computational power, posing a challenge for low power embedded systems. This paper proposes a multitask learning network for traffic object detection, drivable road lane segmentation, and lane line segmentation, which achieved second place in the Low-power Deep Learning Object Detection and Semantic Segmentation Multitask Model Compression Competition for Traffic Scene in Asian Countries. The model is designed for real-time autonomous driving systems with limited computational resources, achieving real-time inference within 20 milliseconds. The proposed model includes efficient backbone and multitask head architecture, customized classes balance, and optimized training loss. We evaluated the proposed multitask YOLO (MT-YOLO) model on several embedded platforms with AI processing units capable of accelerating quantized neural networks. The proposed model considers highly customized heterogeneous hardware, which can meet real-time requirements on multiple platforms while maintaining accuracy.
Transfer Learning Improves Unsupervised Assignment of ICD codes with Clinical Notes

In healthcare industry, it is a standard practice to assign a set of International Classification of Diseases (ICD) to a clinical note (which can be a patient visit, a discharge summary and the like) as part of medical coding process mandated by medical care and patient billing. A supervised framework is adopted for most of the automated ICD coding assignment methods in which a subset of the clinical notes are a-priori labeled with ICD codes. But in lot of cases enough labeled texts are not available. These call for an unsupervised assignment of ICD codes. However, the quality of the data plays an important role in the performance of unsupervised coding, - low quality data leads to degradation of performance. In this paper, we explore a transfer learning approach for ICD coding using a combination of pre-training and supervised fine-tuning. We use a hierarchical BERT model comprising of a Bi-LSTM layered on top of BERT (this removes the restriction on the size of clinical texts)) as part of model architecture, and pre-train it on the total corpus (which include both labeled and unlabeled data). Next we transfer its weights to fine tune the model with labeled data (MIMIC data) in a supervised framework and then use this model to predict ICD code for unlabeled data using token similarity. This is the first use of using transfer learning in ICD prediction to our knowledge. Finally we show the efficacy of our transfer learning approach through rigorous experimentation, - there is 20% gain of sensitivity (recall) and 6% lift in specificity in ICD prediction compared to direct unsupervised prediction.
A Critical Review Of The Effectiveness Of Teaching And Learning In Higher Education

The process of teaching and learning may be characterized as the transfer of skills from teachers to pupils. The process through which a teacher identifies and establishes the learning objectives, develops instructional materials, and puts the teaching and learning approach into action is referred to as a combination of several aspects. However, learning is a crucial component that an educator has to take into account. The purpose of this article was to review pedagogy, inclusive practices, and numerous academic publications in order to evaluate the efficacy of teaching in higher education. The study's goal is to evaluate how well professors educate in a higher education environment. The study analyzed the theory with the use of literary forms and experimental research methodologies (mainly introspection), reinforcing the practice with university experiences. The results of the research point to the introduction of role-playing and the provision of constructive and appropriate exploratory and feedback for growth as having a profoundly good effect on students' confidence and self-esteem. Additionally, it was shown that an inclusive environment fosters inclusiveness and enhances student and teacher academic achievement. The results of the study will make it possible for teachers to assist in developing and implementing an inclusive teaching and learning environment in order to raise learners' expectations and academic achievement.