Reviewerly/exHarmony huggingface dataset:
Color Differentiation of Digital Risks in Teacher Education

Introduction. At the present stage of education digital transformation, the urgent task is being addressed to train educators who are ready to meet the challenges and risks of a changing and unstable digital world. Digital risks differentiating and adjusting the course of digital learning for future educators constitute the study problem and goal. The paper draws attention to the issues of digital transformation in the educational system carried out under conditions of uncertainty, as well as ways and opportunities to adjust the course of digital learning, ensuring the readiness of future teachers for the changes that are coming in the near and distant future. Materials and Methods. Leading research methods: system analysis of digital risks in teacher education, scenario modeling of network interactions and digital learning in basic risk situations, pedagogical experiment. The dynamic set of analytical and predictive judgments of future teachers about the possibilities of overcoming risk situations constitute the experimental materials. In research, digital risks are differentiated by three definable parameters: subjective sense of danger, relative risk and probability of threat. Results. The algorithmic model provides risk classification features and color codes. Their influence on the choice of safe behavior strategies in basic risk situations associated with networking and digital learning is described. The study reveals the authorʼs idea of risks color differentiation, which in this paper is illustrated by examples of teachers’ professional activities in networking and digital learning situations. The paper practical significance lies in pedagogical influences of algorithmic model approbation in risks situations of different danger levels: especially dangerous, moderate and underestimated risks in the dynamic digital space. It is concluded that actions in danger situations can be effectively regulated by light signals similar to a modified traffic light. Each signal can be matched with an individual scenario included a stereotypical behavior algorithms set that is assigned considering the real danger and subjective feeling on it, the basic scenario and prediction corresponding to it. The survey final measurements showed that the future teachers who passed the experimental training significantly decreased their subjective sense of danger. Discussion and Conclusion. The pedagogical experiment reveals that the study of risk situations, pedagogical activity scenario modeling and orientation to color cues in typical risk situations reduce the teachers’ uncertainty sense and give positive shifts in teachers’ training.
Resolving the Ethical Tension Between Creating a Civil Environment and Facilitating Free Expression Online: Comment Reordering as an Alternative to Comment Moderation

Incivility in online commenting sections can create a hostile environment and result in the silencing of vulnerable voices. Accordingly, content websites and social media platforms have an ethical responsibility-one that aligns with their strategic interests-to minimize users' exposure to uncivil content. To this end, platforms invest great effort and budget in automatic and manual filtering mechanisms. Yet, these efforts create a competing ethical quandary, as they often come at the expense of free expression, particularly in cases where comments do not explicitly infringe on stated guidelines but might nevertheless be interpreted as offensive. In this paper, we examine an alternative moderation approach, based on comment reordering as opposed to deletion of uncivil comments. Specifically, we show that exposure to uncivil (vs. civil) comments located in the head or at the bottom of a list of comments increases subsequent commenters' likelihood of posting uncivil comments themselves. Exposure to uncivil comments in the middle of a list, however, does not significantly enhance commenters' likelihood of commenting uncivilly. These results offer new theoretical insight into how incivility is transferred between users in online environments. Our results also suggest a straightforward technological solution for mitigating online incivility, which is more ethical and practical than current industry standards. This involves placing civil comments at the beginning and end of the comment thread, with uncivil comments located in the middle.
An Investigation and Observational Remarks on Conventional Sign Language Recognition

In today’s world, communication of speech and hearing impaired person has been facilitated with different technologies. In the world, there are about 300 million people are deaf, 285 million are blind and 1 million are dumb, as per the World Health Organization. Sign language is a tool to communicate with speech and hearing impaired people. Recognition of sign language is a challenge for researchers from many years that have to be implemented as a system for various sign languages. Each system has its own limitations and difficult to be used commercially. Researchers have done their research in various ways to simplify the recognizing of sign languages with limited database. Researchers are trying to do research with their own large database. Through this paper, we review the different sign language recognition approaches and try to find the best method that has been used. This helps the researchers to retrieve more information to develop the sign language recognition systems using current and advanced technologies in future.
Object detection by the combination of generic roi extractor and dynamic R-CNN with side-aware boundary localization in aerial images

Unmanned Aerial Vehicles (UAVs) have recently gained popularity due to their simplicity and effectiveness in traffic monitoring and potential for rapid delivery, and rescue support. Moreover, UAVs have been employed as a supporting machine in data collection for object detection tasks, in particular vehicle detection tasks in object recognition. Although vehicle identification is a tough problem, many of its challenges have recently been overcome by two-stage approaches such as Faster R-CNN, one of the most successful vehicle detectors. However, many critical problems still remain, such as partial occlusion, object truncation, object multi-angle rotation, etc. In this paper, we combine the Generic RoI Extractor (GroIE) method with Dynamic R-CNN and Side-aware Boundary Localization (SABL) for both testing and evaluation on a challenging dataset XDUAV. Overall, 4344 images in the XDUAV dataset, divided into 3 subsets: 3485 training images, 869 testing images and 869 validating images were used. These consisted of six object classes: 33841 “car”; 2690 “bus”; 2848 “truck”; 173 “tanker”; 6656 “motor” and 2024 “bicycle”. With the ResNet-101 backbone, our approach showed competitive results compared with the original GRoIE method, surpassed by 1.2% on mAP score and by about 2% on most classes AP scores, except for the class 'tanker'.
String Kernel-Based Techniques for Native Language Identification

Abstract In recent years, Native Language Identification (NLI) has shown significant interest in computational linguistics. NLI uses an author’s speech or writing in a second language to figure out their native language. This may find applications in forensic linguistics, language teaching, second language acquisition, authorship attribution, identification of spam emails or phishing websites, etc. Conventional pairwise string comparison techniques are computationally expensive and time-consuming. This paper presents fast NLI techniques based on string kernels such as spectrum, presence bits, and intersection string kernels incorporating different learners such as a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting-XGBoost (XGB). Feature sets for the proposed techniques are generated using different combinations of features such as n-word grams and noun phrases. Experimental analyses are carried out using 8235 English as a second language articles from 10 different linguistic backgrounds from a typical NLP benchmark dataset. The experimental results show that the proposed NLI technique incorporating a spectrum string kernel with an RF classifier outperformed existing character n-gram string kernels incorporating SVM, RF, and XGB classifiers. Also, comparable results were observed among different combinations of string kernels. Interestingly, the random forest classifier outperformed SVM and XGB classifiers with different feature sets. All the proposed NLI techniques demonstrated promising results with significant improvement in training time, with the best result attaining more than a 95 percent decrease in training time. The reduced training time of proposed techniques makes it well suited to scale NLI applications for production.
Differentially Private Recurrent Variational Autoencoder For Text Privacy Preservation

Abstract Deep learning techniques have been widely used in natural language processing (NLP) tasks and have made remarkable progress. However, training the deep learning model relies on a large amount of data which may involve sensitive information like electronic medical records. The attacker can infer sensitive information from the model, which leads to privacy leakage. To solve this problem, we propose a Differentially Private Recurrent Variational AutoEncoder (DP-RVAE) that can generate simulated data in place of the sensitive dataset to preserve privacy. To generate high utility synthetic text, a part of sensitive text data is employed as the conditional input of the model and uses a dropout and noise perturbing mechanism to preserve differential privacy. In addition, we expand the proposed DP-RVAE to a federated learning setting and design a novel training paradigm for NLP tasks. Specifically, DP-RVAE is deployed to the client-side to train and generate personalized text. These DP-RVAE models would be aggregated and updated through the Federated Optimisation (FedOPT) algorithm so that personal information can be well preserved. We evaluate our proposed DP-RVAE through a text classification task on the Tweets depression sentiment and IMDB reviews datasets. Our DP-RVAE achieves a higher average test accuracy by 5.90% and 3.94% compared to the typical centralized training and federated learning approach, respectively. We also perform the keywords inference attack experiment on the medical description dataset collected from the real world. Compared to the typical differentially private preserving approach, the DP-RVAE decreases by 15.2% in average attack accuracy. The experimental results demonstrate that DP-RVAE can be applied to the NLP models to leverage accuracy while preserving sensitive privacy.
Gradient Estimation for Ultra Low Precision POT and Additive POT Quantization

Deep learning networks achieve high accuracy for many classification tasks in computer vision and natural language processing. As these models are usually over-parameterized, the computations and memory required are unsuitable for power-constrained devices. One effective technique to reduce this burden is through low-bit quantization. However, the introduced quantization error causes a drop in the classification accuracy and requires design rethinking. To benefit from the hardware-friendly power-of-two (POT) and additive POT quantization, we explore various gradient estimation methods and propose quantization error-aware gradient estimation that manoeuvres weight update to be as close to the projection steps as possible. The clipping or scaling coefficients of the quantization scheme are learned jointly with the model parameters to minimize quantization error. We also apply per-channel quantization on POT and additive POT quantized models to minimize the accuracy degradation due to the rigid resolution property of POT quantization. We show that comparable accuracy can be achieved when using the proposed gradient estimation for POT quantization, even at ultra-low bit precision.
Geospatial Knowledge Graphs

chapter Share on Geospatial Knowledge Graphs Authors: Nikolaos Karalis View Profile , Eleni Tsalapati View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 241–256https://doi.org/10.1145/3581906.3581921Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Agile methodologies between software development and music production: an empirical study

Over the past 20 years agile methodologies revolutionized Information Technology, offering tremendous opportunities for the development of Software Engineering as an independent discipline. More specifically, agile methodologies contributed to enhancing the effectiveness and the speed of the production process as well as to improving the productivity and motivations of software developers organized in high performing teams. The agile philosophy can be and has been applied in different contexts and across several domains. This work analyses the relationship between Agile methodologies used by software engineers and the practices pursued by musicians in their daily lives. Our findings suggests that collaborative, strongly planned software development life cycle models (such as Waterfall, V-model, iterative, and Spiral) are not adequate models to describe the daily practices of musical composers. This is because their work requires a lot of flexibility, which such models intrinsically lack, because they are oriented to ensuring some form of monitorable progress. Interestingly, our findings also show that nine out of 12 Agile Principles are consciously or unconsciously followed by musicians in their practices. This suggests that there are some deep connections between these two prima facie different fields, which are both very creative. Even though our findings await replication, possibly with larger statistical samples, they contribute to open up a new strand of research in the field.
Monitoring biological degradation of historical stone using hyperspectral imaging

Stone is one of the most common materials used as a building material in central Europe for centuries. Historical objects are endangered by degradation procedures coming from physical, chemical and biological weathering agents.The weathering process itself should be analysed in detail in order to prevent historical objects by application of proper restoration cleaning techniques. Within our research, a historical sandstone block was analysed during time to monitor biological changes on the surface. The object of interest is situated in the immediate vicinity of Charles Bridge in Prague, which is protected as a UNESCO heritage site. This site was chosen due to high overall humidity all year long. For investigation of the year- round process of biodegradation hyperspectral sensor was used. In the first place, data were processed using four vegetation indices (NDVI, RGRI, CRI1 and VREI1). All vegetation indices indicate that vegetation is thriving and subject to normal seasonal change. The second chosen method of data processing is to use spectral reflectance curves and their subsequence processing by Spectral Angle Mapper (SAM) classification algorithms. A decline in vegetation with the onset of autumn and during the winter months was detected.
Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator

Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a recognizer trained to classify Asian bird species cannot recognize the species of kiwi, because the class label “kiwi” and its image samples have not been seen during training. To overcome this limitation, zero-shot classification has been studied recently, and the joint-embedding-based approach has been suggested as one of the promised solutions. In this approach, image features and text descriptions belonging to the same class are trained to be closely located in a common joint-embedding space. Once we obtain the embedding function that can represent the semantic relationship of image–text pairs in training data, test images and text descriptions (prototypes) of unseen classes can also be mapped to the joint-embedding space for classification. The main challenge with this approach is mapping inputs of two different modalities into a common space, and previous works suffer from the inconsistency between the distribution of two feature sets on joint-embedding space extracted from the heterogeneous inputs. To treat this problem, we propose a novel method of employing additional textual information to rectify the visual representation of input images. Since the conceptual information of test classes is generally given as texts, we expect that the additional descriptions from a caption generator can adjust the visual feature for better matching with the representation of the test classes. We also propose to use the generated textual descriptions to augment training samples for learning joint-embedding space. In the experiments on two benchmark datasets, the proposed method shows significant performance improvements of 1.4% on the CUB dataset and 5.5% on the flower dataset, in comparison to existing models.
Classification of ISL Using Pose and Object Detection-Based Techniques

Hearing-impaired individuals are forced to face numerous obstacles in several challenges in different facets of life. The unemployment rate among the deaf is staggering. The COVID-19 pandemic has introduced a new degree of hardship, particularly because the work environment has been completely switched to online platforms because several companies aren’t prepared with the necessary tools to involve the hearing impaired in this change. Societal inclusion and acceptance of differently-abled people are dependent on the ecosystem that has been created for them, which involves training and skilling them as well as understanding and seamlessly interacting with them, hence increasing employability. With the aid of this project, we want to bridge the communication gap for the hearing impaired and thereby contribute to the development of suitable habitats for them. In this paper, we present two approaches for the classification of Indian Sign Language: (a) the object detection-based approach utilizes a model built on Scaled-YOLOv4 architecture which performs a frame-by-frame inference and (b) the Pose-based approach utilizes an LSTM model which takes the skeletal pose landmarks from Mediapipe for a sequence of frames as an input to infer and predict the action.
Assessing the Effectiveness of Masking and Encryption in Safeguarding the Identity of Social Media Publishers from Advanced Metadata Analysis

Machine learning algorithms, such as KNN, SVM, MLP, RF, and MLR, are used to extract valuable information from shared digital data on social media platforms through their APIs in an effort to identify anonymous publishers or online users. This can leave these anonymous publishers vulnerable to privacy-related attacks, as identifying information can be revealed. Twitter is an example of such a platform where identifying anonymous users/publishers is made possible by using machine learning techniques. To provide these anonymous users with stronger protection, we have examined the effectiveness of these techniques when critical fields in the metadata are masked or encrypted using tweets (text and images) from Twitter. Our results show that SVM achieved the highest accuracy rate of 95.81% without using data masking or encryption, while SVM achieved the highest identity recognition rate of 50.24% when using data masking and AES encryption algorithm. This indicates that data masking and encryption of metadata of tweets (text and images) can provide promising protection for the anonymity of users’ identities.
Incomplete Geospatial Information

chapter Share on Incomplete Geospatial Information Author: Charalampos Nikolaou View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 199–220https://doi.org/10.1145/3581906.3581919Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Comparative Analysis of Automatic Speaker Diarization Techniques

Speaker diarization is the process of determining “who spoke when?” in a piece of audio that has an ambiguous amount of speech from an ambiguous proportion of participants. At first, it was suggested as a study area for automatic speech recognition, where feature extraction and upstream processing step speaker diarization are both included. In addition, we examine open source diarization toolkits, approaches, free datasets, and a description of the ongoing advancement and use of the diarization system in this study.
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models. While many real-world applications require dynamic models that constantly adapt over time and react to changes in the underlying distribution, XAI, so far, has primarily considered static learning environments, where models are trained in a batch mode and remain unchanged. We thus propose a novel model-agnostic XAI framework called incremental PDP (iPDP) that extends on the PDP to extract time-dependent feature effects in non-stationary learning environments. We formally analyze iPDP and show that it approximates a time-dependent variant of the PDP that properly reacts to real and virtual concept drift. The time-sensitivity of iPDP is controlled by a single smoothing parameter, which directly corresponds to the variance and the approximation error of iPDP in a static learning environment. We illustrate the efficacy of iPDP by showcasing an example application for drift detection and conducting multiple experiments on real-world and synthetic data sets and streams.