Reviewerly/exHarmony huggingface dataset:
Transfer Learning Based Youtube Toxic Comments Identification

Online users are negatively affected by the spread of offensive content on social media sites. A fear, dislike, unease, or distrust of lesbian, gay, bisexual, or transgender persons is known as homophobia or transphobia. Homophobic/transphobic speech, which can be summed up as bigotry directed towards LGBT+ people, has grown to be a significant problem in recent years. The major social problem of online homopho- bia/transphobia threatens to eliminate equity, diversification, and acceptance while also making online places toxic and unwelcoming for LGBT+ people. It is found to be sensitive subject and untrained crowd sourced annotators have trouble in identifying homophobia due to cultural and other preconceptions. As a result, annotators had been educated and provided them with thorough annotation standards. 15,141 multilingual annotated comments make up the dataset. The proposed work identifies the best Machine Learning Classifier with BERT embedding model for the Code-Mixed Dravidian Languages in order to identify the toxic languages directed towards LGBTQ+ individuals. Adaboost classifier outperforms other three classifiers in terms of accuracy.
A Parsing Tool for Short Linguistic Constructions

In the current trend of unstructured language usage in simpler and shorter conversations, we identified a need of language analysis tool (a language parser) to find semantic relations within the sentence constituents. Earley’s algorithm is used in this paper to propose a parsing approach based on the Tree Adjoining Grammar (TAG) formalism. It is able to extract dependency relations in the derivation tree form. A generalized TAG tree-grammar pertaining to Subject-Object-Verb (SOV) syntactic pattern is also presented corresponding to the proposed algorithm. This grammar is targeted to be suitable for short dialogues in low resource languages which have scarcity of enough data for ML/DL based approaches. The process flow is presented using the combinatory tree-set formation and parsing. Two implementations are carried out using XML and LISP notation data structure. Examples from an Indian language are presented in a step-by-step manner that illustrates parsing in a controlled grammar environment for linguistically not-very complex syntactic constructions. Results along with findings are presented for execution time and memory consumption.
Correlating Copula Constructions in Tamil and English for Machine Translation

Translation between language pairs of linguistic divergence is complex. Human mind can establish correlations that exist between the linguistically divergent languages. But making a machine to understand this correlation is quite difficult. The complexity arises while finding the equivalent replacement from source language to target language. Copula construction in which the subject is linked to its complement in the predicate position by a link verb known as copula verb is found in many languages with different realizations. Tamil makes use of mainly two types of copula construction: one is zero copula construction and another is verbal copula construction. English does not make use of zero copula construction; it makes use of only verbal copula construction. When we try to correlate Tamil copula constructions with English copula constructions or vice versa we come across a lot of mismatches and complexities. In this paper we discuss about the realization of these mismatches which are crucial for Machine Translation between Tamil and English and vice versa.
Human-Centered Explainability for Intelligent Vehicles—A User Study

Advances in artificial intelligence (AI) are leading to an increased use of algorithm-generated user-adaptivity in everyday systems. Explainable AI aims to make algorithmic decision-making more transparent to humans. As future vehicles become more intelligent and user-adaptive, explainability will play an important role in ensuring that drivers understand the AI system’s functionalities and outputs. However, when integrating explainability into in-vehicle features there is a lack of knowledge about user needs and requirements and how to address them. We conducted a study with 59 participants focusing on how end-users evaluate explainability in the context of user-adaptive comfort and infotainment features. Results show that explanations foster perceived understandability and transparency of the system, but that the need for explanation may vary between features. Additionally, we found that insufficiently designed explanations can decrease acceptance of the system. Our findings underline the requirement for a user-centered approach in explainable AI and indicate approaches for future research.
Improved Gaussian filtering for handling concurrent delayed and missing measurements

Abstract This article addresses the Gaussian filtering problem under the environment of jointly occurring delayed and missing measurements. In this work, the former irregularity is incorporated (in the measurement model) using a Bernoulli random variable (BRV) and a geometric random variable, while the latter is subsumed with the help of the BRV; thereby, it enables to take account of large delay extents efficiently. Specifically, a modified measurement model, which incorporates the concerned irregularities, is introduced. Accordingly, the measurement‐related statistical parameters, that is, measurement estimate, covariance, and cross‐covariance, are rederived with respect to the modified measurement model. The rederived parameters replace the corresponding ones in the traditional Gaussian filtering algorithm, resulting in the proposed Gaussian filter. The simulation results conclude the superior performance of the proposed filter over the existing filters in handling the coexisting delay and missing measurement irregularities.
Some observations on generative text artificial intelligence’s impact on libraries part 2

Purpose The purpose of this study is to provide a series of observations to help information professionals understand and anticipate the future developments. Design/methodology/approach This column delves into artificial intelligence (AI)-powered search and its implications for the open Web to show how multifaceted the impact of generative text AI might be in a single arena. Findings This framework includes the following observations: future applications are unpredictable, interdependent and will require flexibility, AI’s ability to create the feeling of intelligence combined with the power of language are key to its future; the technology is not neutral, it has biases and significant limitations; its widespread adoption will come with trade-offs that matter; and that libraries should develop a positive vision for how to use it. Originality/value For libraries to serve their patrons or embody their values, they must be prepared to grapple with a rapidly evolving technology by understanding the technologies possibilities, what makes it compelling, as well as its limitations and dangers.
Predicting the extent of andic soils across western Haleakalā, Maui

Abstract Maui is one of five Hawaiian Islands affected by orographic climate effect, exhibiting a massive precipitation gradient across western Haleakalā. However, high variability of volcanic ash deposits as a parent material across the study area complicates the ability to isolate the influence of climate on soil formation. Little is documented about the spatial extent of ash deposition, frequency and intensity of volcanic ejecta events, and composition of ash. Therefore, andic soils, which contain short range order (SRO) aluminosilicates and iron oxides that result in unique soil chemical and physical properties, are challenging to map. Using environmental and andic property data from 16 pedons sampled in the study area—bulk density, phosphate retention, and aluminum plus ½ iron extracted by ammonium oxalate—we applied multiple linear regression to create spatial prediction models of these three soil properties. Predicted soil properties were then used to classify andic soils. The mean prediction for an independent set of pedons showed a soil classification accuracy of 50% in the study area for Andisols (data to 60 cm), andic intergrades (data to 75 cm), and non‐andic soils. Soil property predictions using depth‐weighted average data to 1 m increased soil classification user accuracy of Andisols to 87.5%, andic‐intergrades to 100%, and non‐andic soils to 83.3%. Whether a soil exhibits andic soil properties within 60 or 75 cm is irrelevant when considering prior or current presence of ash in a soil. Accounting for all available pedon data with depth proves most important when attempting to predict andic properties and classes.
An interactive web-based solar energy prediction system using machine learning techniques

Solar energy being one of the most inexpensive renewable energy sources, has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation. For the purpose of creating a more trustworthy and successful energy planning strategy, accurate projections of sun irradiation, solar energy generation, and revenues are crucial. Hence, in this work we have proposed web-based optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques. Furthermore, an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption, production, solar radiation, and investment potential for a specific county in California. The model's performance has been measured using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE) scores. Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE, MSE, and MAE.
Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation

This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian–English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.
Practical Consequences of Quality Views in Assessing Software Quality

The authors’ previously published research delved into the theory of software product quality modelling, model views, concepts, and terminologies. They also analysed this specific field from the point of view of uncertainty, and possible descriptions based on fuzzy set theory and fuzzy logic. Laying a theoretical foundation was necessary; however, software professionals need a more tangible and practical approach for their everyday work. Consequently, the authors devote this paper to filling in this gap; it aims to illustrate how to interpret and utilise the previous findings, including the established taxonomy of the software product quality models. The developed fuzzy model’s simplification is also presented with a Generalized Additive Model approximation. The paper does not require any formal knowledge of uncertainty computations and reasoning under uncertainty, nor does it need a deep understanding of quality modelling in terms of terminology, concepts, and meta-models, which were necessary to prepare the taxonomy and relevance ranking. The paper investigates how to determine the validity of statements based on a given software product quality model; moreover, it considers how to tailor and adjust quality models to the particular project’s needs. The paper also describes how to apply different software product quality models for different quality views to take advantage of the automation potential offered for the measurement and assessment of source code quality. Furthermore, frequent pitfalls are illustrated with their corresponding resolutions, including an unmeasured quality property that is found to be important and needs to be included in the measurement and assessment process.
Comparisons of Emotional Responses, Flow Experiences, and Operational Performances in Traditional Parametric Computer-Aided Design Modeling and Virtual-Reality Free-Form Modeling

Three-dimensional (3D) computer-aided design (CAD) is a vital tool for visualizing design ideas. While conventional parametric CAD modeling is commonly used, emerging virtual reality (VR) applications in 3D CAD modeling require further exploration. This study contrasts the emotional response, flow experience, and operational performance of design novices using VR free-form modeling (Gravity Sketch 3D) and conventional parametric CAD modeling (SolidWorks). We arranged two representative tasks for 30 participants: modeling an exact geometric shape (a cube) and a creative shape (a mug). We measured emotional response and flow experience through scales, and gathered operational performance, and further insights through semistructured interviews. The findings reveal more positive and intense emotional responses to VR free-form modeling, but its overall flow experience did not exceed expectations. No significant differences were found in concentration, time distortion sense, or control between the two techniques. Comparing modeling tasks, VR free-form modeling showed promising operational performance for early ideation, whereas conventional parametric CAD modeling proved to be more effective in 3D digitization of known shapes.
SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, and accurate illness monitoring. Powered wheelchairs (PW) with sensors, computers, and other connected assistive technologies are called smart wheelchairs. Smart wheelchairs with sensing abilities are intended to offer universal connectivity using cloud and edge computing technology. Numerous outstanding people were impacted by paralyzing phenomena, including Stephen Hawking and Max Brito. The issue of legitimacy is one of the most important difficulties in e-health applications, because of how sensitive the technology is, and this needs to be appropriately handled. To safeguard the data transport, usage, and interchange between sensor nodes/smart wheelchairs and servers, e-health applications require an authentication method. As all conversations use wireless channels, e-health apps are exposed to various vulnerabilities. Additionally, the IoT has limited computational and power capacity limitations. To combat the various security risks, the present research offers a user authentication technique that is efficient and ensures anonymity. The suggested method creates a safe connection for the authorized entity and forbids unauthorized entities from accessing the Internet of Things sensor nodes. The suggested approach has lower communication and computation overheads than the traditional techniques, making it more effective. In addition, the security verification of the presented protocol is scrutinized through AVISPA. The proposed scheme, on average, requires only 12.4% more computation cost to execute. Compared to the existing approaches, the suggested protocol’s extra computational cost can be compensated for by its enhanced security, while the suggested method’s communication cost is 46.3% smaller.
Could Vocational Education Benefit From Augmented Reality and Hypervideo Technologies? An Exploratory Interview Study

Context: This study investigates the perspective of vocational educators on the possibility of adopting augmented reality (AR) and hypervideo (HV) technologies to support their teaching practice. Vocational education and training (VET) is particularly concerned with the learning of resources (knowledge, skills and attitudes) that are immediately transposable into conduct and procedures in the workplace. AR and HV can provide means to answer this requirement, but both technological solutions are still not so diffused in VET. The purpose of this study is to inquire into the perception of educators on the main advantages and disadvantages of using AR and HV to support teaching-and-learning. Methods: A semi-structured interview protocol has been proposed to 73 teachers, intercompany trainers and in-company trainers in 10 professions (at least two per category within each profession). The interview was organized in two main steps: A need analysis, in which the most important and difficult operative skills are identified for the interviewee's profession; and a discussion of advantages and disadvantages of AR and HV. Content analysis was applied to the interview transcriptions. Results: The results show that the main advantages reported in the literature for the two technologies – such as the ability to switch between 2D and 3D and carry out simulations – are also found in the VET context by educators. For HV the main technical advantages (such as the use of active points, and non-linear navigation of video content) were autonomously recognised, while the potential of the instrument to support reflection has not been clearly identified. Conclusions: AR and HV are considered as tools able to support apprentices' procedural learning especially with regard to the operational skills which were judged by the educators to be most relevant for VET.
Style Transfer Based on VGG Network

Abstract With the rapid development of computer computing power, as an important method in the field of artificial intelligence, deep learning has amazing learning ability, especially in dealing with massive data, which makes deep learning in the fields of image recognition, image classification, natural language processing, data mining and unmanned driving, Has shown an extraordinary role. In previous studies, the style transfer algorithm has not developed well due to the poor computing power of Computer, the basic configuration of computer hardware can not meet the minimum requirements and the poor image effect after migration. However, with the development of computer hardware and the rapid change of GPU computing power, the style transfer network based on deep learning has become a hot issue in the study of style transfer in recent years. According to the research, although the traditional style transfer method can obtain the texture, color and other information of the style image, the model needs to be learned every time a new target image is generated, and the time cost during this period is very high. In this way, the trained model is not repeatable, and the generated image is often very random and can not get good results. Therefore, the emergence of style transfer methods based on deep learning solves the limitations of traditional style transfer methods. Style transfer methods based on deep learning are faster than traditional style transfer methods, and the generalization of the model is better. The style transfer algorithms of main neural networks are divided into two categories, Slow style transfer based on image iteration and fast style transfer based on model iteration. VGG network model can combine style image and content image, and greatly improve the style transfer efficiency of image.
Comparison of Discrete Wavelet and Discrete Cosine Transform for Enhancement of Gray Image

In this paper comparison of Discrete Wavelet and Discrete Cosine Transform for contrast enhancement of gray image is discussed and implemented using proposed mask. The technique converts the image into DCT domain and the DCT coefficients are modified using proposed mask then the enhanced image is reconstructed using inverse DCT. After comparing enhancement based on DCT with enhancement based on wavelet transform I found that the discrete cosine transform outperforms with better image quality and with highest PSNR value.
A similarity-based Bayesian mixture-of-experts model

Abstract We present a new nonparametric mixture-of-experts model for multivariate regression problems, inspired by the probabilistic k -nearest neighbors algorithm. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point, yielding predictive distributions represented by Gaussian mixtures. Posterior inference is performed on the parameters of the mixture components as well as the distance metric using a mean-field variational Bayes algorithm accompanied with a stochastic gradient-based optimization procedure. The proposed method is especially advantageous in settings where inputs are of relatively high dimension in comparison to the data size, where input–output relationships are complex, and where predictive distributions may be skewed or multimodal. Computational studies on five datasets, of which two are synthetically generated, illustrate clear advantages of our mixture-of-experts method for high-dimensional inputs, outperforming competitor models both in terms of validation metrics and visual inspection.
Optimization of Photovoltaic Systems for Two Different Regions in Bosnia and Herzegovina Covering Electricity Demand of a Typical Household

The paper presents the optimization of photovoltaic systems to cover the electricity needs of a typical household for two climatic regions in Bosnia and Herzegovina, whose representatives are the cities of Sarajevo and Mostar. The solar potential was measured and downloaded from the PV GIS database in order to compare the measured and downloaded values. The optimization showed that the cost-optimized systems for both regions should have 13 kW of installed PV panels with connection to the grid without the use of batteries. The optimization also showed a lower consumption of electricity from the grid for the Mostar area as well as a reduction in carbon dioxide emissions in the amount of 10 kg annually.
Renewable Energy Sources Photovoltaic Power Plants and Constructive Solutions Related to Locality and Climate

Energy consumption is increasing every day, which is why renewable energy is increasingly being offered as one of the most important solutions in the fight against climate change and the energy crisis. Exactly one of these forms is solar energy. Solar radiation is directly converted to heat or electricity. The conversion is done by different devices, some of which are photovoltaic cells for generating electricity. This paper shows how to increase the energy efficiency of photovoltaic power plants with a somewhat different approach to constructive solutions of load-bearing structure. Constructive solutions in this paper are causally related to the geographical position and the local climate. A particularly constructive solution was made in the paper with technical and economic analysis.