Reviewerly/exHarmony huggingface dataset:
Tool for Digital Inclusion in Italian Schools: The Use of Self-Assessment Framework from ENTELIS Project

The use of technology in education has the potential to create more inclusive environments for all students, including those with disabilities. In Italy, the Ministry of Education has recognized the importance of digital literacy in schools and has issued several documents outlining the steps necessary to build a more inclusive educational system. However, in order to achieve these goals, it is important to assess the current situation and identify areas for improvement. The Entelis Self-assessment framework, particularly the Italian short version, can be used as a tool to help schools assess their digital inclusion practices and develop strategies for improvement. In 2015, the Ministry of Education issued the new Law 107/2015, called “La Buona Scuola”, which included the New National Plan on School Digital Literacy – PNSD (MIUR, 2015) and the Three-year plan of in-service teachers’ education and training. These documents aimed to consolidate the path of innovation and digitization in schools through a strategy that invested in the technological, epistemological, and cultural dimensions. The PNSD focused on the initial and in-service training of teachers, recognizing that educational innovation through digital technologies requires a reformulation of traditional teaching-learning methods to include pedagogical accessibility and inclusion. However, the success of these initiatives depends on the readiness of teachers to implement new technologies in their classrooms. The European Union (EU) identified teacher training as a critical factor for the success of ICT-related educational innovation in 2003. Teachers themselves recognize the need for training, with TALIS results (OEDC, 2014) showing that the need for training related to teaching with information and communication technology (ICT) skills and using new technologies in the workplace was a priority for teachers.
Evaluation of Commercially Available Fall Detection Systems

Falls are a serious problem in the hospital setting and home environments. However, this problem does not only affect the elderly, but also people who have had surgery, have disabling problems, have associated diagnoses (such as poor eyesight, confusion, etc.) or are dizzy or have walking aids. The aim of research was to find, compare and implement fall detectors especially for the hospital environment. This paper summarizes possible fall detectors. Various technological solutions were selected for testing, including wearable technologies as well as contactless technologies based on PIR detectors and mmWave technologies. The selected fall detectors were tested in living laboratory of HEALTHLab.vsb.cz and then in Hospital AGEL Třinec – Podlesí. The best result of the testing was the use of two Vayyar Home Care devices in one room, thus achieving a detection accuracy of 92.50 % and a sensitivity of 92.50 %.
Head Gesture Interface for Mouse Stick Users by AAGI

We developed a gesture interface (AAGI) for individuals with motor dysfunction who cannot use standard interface switches. These users have cerebral palsy, quadriplegia, or traumatic brain injury and experience involuntary movement, spasticity, and so on. In this paper, we describe a disabled user who utilizes a mouth stick for laptop PC input in daily life. Our objective is to lower the burden on his body by using gestures. To this end, we developed a “home position” for the head that enables gestures to coexist with the mouse stick usage. The results of basic experiments with five healthy participants indicate that our system has reached the level where it can be applied to actual disabled persons. Finally, we applied the system to a user with cerebral palsy asked him to perform web browsing.
Health technology assessment-based evaluation study of a surgical imaging system with a built-in-LED-based intelligent temperature balance control algorithm

Currently, intelligent algorithms are widely used in the field of image processing to reduce the impact of fog on images. Surgical imaging systems are medical imaging devices used in surgical operations. With the continuous improvement of medical imaging technology and the application of advanced intelligent algorithms in medical devices, laparoscopic surgery is increasingly replacing traditional open surgery. However, due to the difference in temperature inside and outside the abdomen, lens fogging during laparoscopic surgery can affect the surgical field of view and interfere with the surgical process. The intelligent temperature balance control system, as a new technology, aims to improve the imaging field of view at different temperatures by combining hardware design with intelligent imaging algorithms. However, there is currently no evidence in the literature to support the anti-fog effect of surgical imaging systems equipped with this function. Therefore, this study aims to evaluate the advantages of surgical imaging systems equipped with the intelligent temperature balance control algorithm in clinical applications through comparative testing experiments and the use of Health Technology Assessment (HTA) tools and methods.
Application of Kalman Filtering Approach on Signal Angle-of-Arrival Estimates

This study focuses on modifying the Kalman Filtering (KF) approach, whose measurement noise suppression capability had been proven many times, especially in multidimensional positioning problems, to serve one-dimensional signal angle-of-arrival (AoA) measurements, and examining the overall AoA estimation performance. In order to evaluate the AoA estimation performance of the proposed KF model via the mean square error (MSE) metric, median filtering and smoothing approaches that have been employed in similar problems and systems are examined. The estimation performances of the filtering approaches have been demonstrated via the computer simulations in MATLAB environment. It was shown that the proposed KF model stands out as a successful approach in terms of measurement noise and outlier suppression.
DidEye: An Attempt for Defining and Incorporating Visual Intuition for Convolutional Neural Networks

Is it possible to formally define one of the important capabilities of human mind, the intuition, in a mathematical sense? Can we use this definition to develop more robust CNN (Convolutional Neural Networks) model? Through this method, can we develop an algorithm that can recognize incomplete images? In this study, we attempt to find partial answers to the above questions. First, we examined how the performance of CNN algorithms decreased by reducing the amount of information in the test set images in a controlled manner. In order to reduce this decrease, we made a mathematical definition of intuition that could enrich the convolutional neural networks. We used this definition and the intuition module we created to improve the filter outputs in the convolution layer. We used MNIST number dataset to measure the performance of this new CNN model, enriched with an intuition module, called, DidEye performance . Experimental results show that the suggested DidEye model is much more robust and provides higher performance compared to the classical CNN model in a test set containing incomplete images.
Content Based Image Retrieval on Satellite Imagery

In this study, a content-based image retrieval system for satellite imagery is proposed. The main purpose of the paper is to find the most relevant images to the query image from a massive satellite imagery database using deep learning methodology. For this purpose, graph convolutional network and hash network are combined for image retrieval tasks. Graph convolutional network is used for graph-based image representation. The graph-based image representations are fed into multi-layer perceptron to learn hash code of the image. In this way, hash code based final image representations are obtained. The image searching is based on nearest neighbor approach with the use of these representations. The proposed model is applied to UC Merced and BigEarthNet datasets with extensive experiments.
Public Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling

Social media has become a valuable data source for gathering and analyzing public opinion on products and services. Among the popular social media platforms, Twitter stands out for its ability to provide place-time information in a text format called tweets. In this study, sentiment analysis and topic modeling of tweets related to public transportation in the United Kingdom were analyzed. Using the Robustly Optimized BERT Pretraining Approach (RoBERTa), tweets are divided according to their polarities: positive, neutral, and negative. Additionally, Latent Dirichlet Allocation (LDA) is applied to positive and negative tweets, and topics providing the causes are obtained. These topics reveal the strengths and weaknesses of the United Kingdom's public transportation service.
G-HCF: Product Recommendation by GNN Based Hybrid Collaborative Filtering

In general, product recommendation by collaborative filtering approaches aim at learning an optimum mapping between customer and product representations. The main input they use is the customer-product matrix. These approaches, at best, utilize the customer-customer and product-product relations indirectly. The proposed method uses a customer-product graph, enriched by the customer-customer and product-product relations, with graph neural networks to predict the customer-product relations. These relations are extracted from data by matrix decomposition. Comparative assessment on real data showed improved performance compared to pure matrix decomposition and standard graph models.
Graph Neural Networks Based Approach for Interpersonal Relationship Classification in Images

Interpersonal relationships, which are an integral part of our social life, demonstrate how people connect and interact within society. Describing the relationship between two individuals in images depends on many different factors and attributes. In this study, a Graph Neural Network (GNN) based approach is proposed that focuses on the important attributes for describing the relationship between two individuals in images. In the proposed method, each attribute that will be used to describe the relationship is defined as a GNN node. Then, the meaningless connections between nodes are pruned with a pruning operation to obtain the ideal GNN model. In this study, a more robust GNN for classifying interpersonal relationships is created by using rich attributes, unlike the literature, and obtaining significant connections between nodes through pruning. The experiments conducted in this study showed that both using a wider GNN and pruning operation improve the classification performance.
Transformer-Based Bug/Feature Classification

Automatic classification of a software bug report as a 'bug' or 'feature' is essential to accelerate closed-source software development. In this work, we focus on automating the bug/feature classification task with artificial intelligence using a newly constructed dataset of Turkish software bug reports collected from a commercial project. We train and test support vector machine (SVM), k-nearest neighbors (KNN), convolutional neural network (CNN), transformer-based models, and similar artificial intelligence models on the collected reports. Results of the experiments show that transformer-based BERTurk is the best-performing model for the bug/feature classification task.
Very Large Scale Distributed Training Data and Federated Learning: A Case Study in DAS

FOTAS is an optoelectronic system used to measure acoustic signal values along the length of a fiber optic cable. By creating an acoustic profile along the length of the fiber optic cable, the system uses deep learning to detect and classify events in the physical environment. It provides high detection accuracy over long distances, it is reliable against electromagnetic interference, and it is also reliable during harsh working conditions. In order to train deep learning models known to the FOTAS system, the data must be stored in a central location. However, the migration of data is difficult because of its large size. After installing sensors in various regions and collecting data, it is very expensive to migrate this data to a server for model training. In addition to the difficulties that may emerge from data migration, problems related to data security may also occur. Federated learning enables model training on a given device by using data from server and client devices, which eliminates the need for a problematic data migration process. Client devices process data locally and share model weights with the server device. Therefore, there is no need to transfer large amounts of data. In this study, we apply the federated learning method on the FOTAS system and compare the results with the results from using classical methods. By selecting two client devices and one central device, we transferred model weights between remote devices. By choosing asynchronous device features, we observed how the federated learning system responds to asynchronous conditions. Based on the results of our study, we can conclude that federated learning can be used in commercial systems.
Glare Effect Analysis On Solar Panels

The use of solar power plants (SPP), which is one of the renewable energy sources, has attracted great interest in recent years. Although it is a clean energy source, there is a possibility that the reflection effects that it may create in the regions where it is installed may adversely affect the field of view of land and air vehicle users in that region. In this paper, an approach (SPP Glare Analysis, SGA) developed to calculate the glare effect from solar panels according to location, time and panel parameters is introduced. The sample results obtained with the SGA were compared with the outputs of Forge Solar's SGHAT software, which is accepted as a reference in the literature in terms of glare analysis, and the analysis was verified.
Extreme Learning Machines as Equalizers on Optical OFDM Systems

Optical Fiber Radio (RoF) systems based on OFDM meet the needs of high transmission and reception speeds, as well as offering greater reliability in the system. These systems are exposed to various disturbances, such as the thermal and shot noise of the photodetector, the amplified emission of optical links, and the relative phase intensity in the optical oscillator. To partially address these drawbacks, techniques such as multi-carrier modulation (OFDM), pilot-assisted equalization (PAE), and typical filters have been used. Recently, Extreme Learning Machines (ELM) have been employed instead of classic digital signal processing in RoF-OFDM systems to tackle physical limitations. ELMs are learning algorithms that have low latency rates and the ability to process large volumes of data. This article presents a review and comparison of the main research studies that have utilized ELM. It should be noted that ELM-C achieved the shortest equalization time in most cases compared to other algorithms.
Social and Categorical Signals in Contrastive Learning for Recommendation Systems

Social recommendation systems that use graph neural network (GNN) models are effective in addressing the data sparsity issue present in collaborative filtering models. Social homophily and item similarities are critical factors that affect users' preferences, and GNN models must capture these factors while incorporating users' interaction behaviors. In this work, we propose SCCL, a recommendation model that jointly captures social influence and item similarity signals with crossview contrastive learning.We constructed a user-user social graph from social networks and item-item graphs from common tags. In our model, user-user relations are represented as a homogeneous graph, and item-item relations are represented as hypergraphs. We demonstrate the effectiveness of our model on two real-world datasets.
Anomaly Detection in In-Vehicle Networks with Graph Neural Networks

In today's vehicles, various functions required for the vehicle are managed by Electronic Control Units (ECU) and the communication of these units takes place over the in-vehicle network. With the increase in the number and complexity of vehicle functions, the number of ECUs also increases and in-vehicle network message traffic becomes more complex. Detection of anomalies in in-vehicle network message traffic for the detection and prediction of problems in vehicles has become an important research problem. In the literature, time series analysis based solutions are suggested for this problem. On the other hand, graph-based machine learning and anomaly detection studies have come to the fore recently. In this study, a graph neural network (GNN)-based solution is applied for anomaly detection on in-vehicle network messages. The analyzes on the driving simulation data showed that the GNN-based solution produces successful results for anomaly detection on in-vehicle networks.
Roadside Barrier Detection and Tracking with Radar: A Real Life Application

Perception is one of the key elements in the autonomous driving systems. Especially, it is vital to track the states of the vehicles and the obstacles around the ego vehicle to avoid collision or accidents. These surrounding objects which are detected by radars that are integrated in the outer part of the autonomous driving vehicles. However, the measurements of the static obstacles such as barriers and guardrails are interfering with the measurements of the surrounding vehicles nearby the ego vehicle. Hence,it is essential to develop an algorithm which can remove the barrier and guardrail measurements. In this paper, a heuristic algorithm is developed in order to detect the barrier data which are among all the measurements and distinguish them from the relevant object measurements detected by radars. The algorithm detects the barrier measurements with 1D clustering and fits curves to the measurements, then tracks the already fit barrier curves with individual Kalman Filters. Once the filtered barriers are established, the algorithm removes the measurements that are in a distance from the tracked barriers.
Improving Hate Speech Detection: The Impact of Semantic Representations and Preprocessing Techniques

Social Media is one of the important tools that can be used to measure the pulse of a society. However, when hate speech targeting an individual or group is produced through this tool, this situation be-comes a phenomenon that can lead to social problems. In this context, the detection of hate speech is crucial. In this study, which is proposed for the hate speech detection shared task at SIU 2023 NST, the importance of semantic representations obtained through the Ope-nAI API is investigated in order to detect hate speech effectively. As preprocessing steps, the normalization of the dataset, an emoji dictionary, and SMOTE technic for the problem of imbalanced dataset have been app-lied. To demonstrate the importance of this step for the problem, basic machine learning techniques, SVM and cosine similarity, are being utilized. The experimental results show that the semantic representations offer a successful solution to the problem with machine learning models. In particular, the solution of the pre-processing step applied for the imbalanced dataset has a great contribution to the problem.
Size-based Adaptive Instance Pruning for Refined Segmentation of Cell Nuclei in Histology Images

In this paper, a size-based instance pruning approach, which can be used for more accurate segmentation of cell nuclei in histology images and can be adapted to cell types, is presented. In the proposed study, different cell nuclei types in colon histology images were initially segmented semantically using the U-Net medical image segmentation method and the output segmentation maps were produced for each nuclei type. Then, the Watershed algorithm was employed on the segmentation maps and cell nuclei instances were obtained. Finally, instance segments that were below the acceptable nuclei sizes for each nuclei type and were not classified as nuclei instances were eliminated with the adaptive size-based instance pruning approach. In the tests performed on the CoNIC 2022 dataset, it was observed that the adaptive size-based instance pruning approach outperformed the normal segmentation methodology without this approach, and an average value of 0.5090 mPQ was measured.
Zero and Few-Shot Hate Speech Detection in Social Media Messages Related to Earthquake Disaster

Microblog platforms provide active communication channels during crisis situations such as earthquakes. Data from these platforms plays an important role in alleviating such crises. In times of crisis, it is important to develop quick and reliable methods. In this study, we perform hate speech detection in earthquake-related social media messages using zero-shot and few-shot methods that work with minimal labeled data. We collect Turkish messages shared on Twitter from the 2023 Turkey-Syria earthquake and label them according to their hate speech content. First, we evaluate the zero-shot performance of the proposed models that are trained on a different hate speech dataset on the newly created earthquake dataset. Then, using the newly labeled data, we fine-tune the models with few-shot learning, and evaluate their performance on the same dataset. The results show that transformer-based language models perform better than traditional models when there is not enough labeled data, and the few-shot method outperforms the zero-shot method. Additionally, we examine the effect of varying the size of the training data used for few-shot learning.
NER2Map: Positive Transfer from Named Entity Recognition to Address Parsing in Low Data Regimes

In natural language processing, the time and human resource costs associated with data labeling have led to a focus on solving problems with limited data. In this study, two separate models for Address Parsing were developed using transfer lear-ning methods. Specifically, the BERT model was first fine-tuned on the NER dataset, and then the effects of fine-tuning on a relatively small Address Parsing dataset were examined, resulting in a method called NER2Map. Experimental results show that the NER2Map model achieves higher accuracy in predicting correct labels with limited data compared to directly fine-tuned BERT Address Parsing model. Additionally, the importance of transfer learning in this problem and the ability to achieve good results using a small amount of data have been observed.
Finding Islands on Encrypted Graphs

Cloud computing services have found widespread use recently. Offloading computations to public clouds has many benefits albeit harming the privacy of users and data. Homomorphic encryption facilitates cloud computing services that can do computations over encrypted data without requiring decryption and this enables privacy-preserving applications. In this paper, we propose an approach for confidentially finding islands (connected components) in a graph. We present various performance evaluation results and show that privacy-preservation can be achieved with a cost of computation overhead.