Reviewerly/exHarmony huggingface dataset:
The influence of hyperparameter tuning on machine learning model performance: A theoretical exploration

The optimization of hyperparameters stands as a pivotal aspect in enhancing the performance of machine learning models. This review paper delves into the intricate realm of hyperparameter tuning and its profound impact on the efficacy of various machine learning algorithms. The investigation encompasses a theoretical exploration, shedding light on the nuanced interplay between hyperparameter configurations and model performance. In the burgeoning field of machine learning, the success of a model hinges on the judicious selection of hyperparameters-parameters external to the model itself that govern its learning process. This paper systematically examines the underlying principles of hyperparameter tuning, elucidating its significance in fine-tuning model behavior to meet the demands of diverse datasets and tasks. The review commences with a comprehensive overview of the major hyperparameters influencing model performance, such as learning rates, regularization terms, and architectural parameters. Subsequently, it delves into the intricate relationships between these hyperparameters, dissecting their impact on model convergence, generalization, and robustness. Theoretical frameworks are presented to unravel the mathematical underpinnings of hyperparameter tuning, providing a deeper understanding of the optimization landscape. The paper also explores state-of-the-art hyperparameter optimization techniques, including grid search, random search, and Bayesian optimization. A critical analysis of the advantages and limitations of each method is presented, offering insights into the trade-offs involved in selecting an appropriate optimization strategy. Furthermore, the review scrutinizes the transferability of hyperparameter configurations across different datasets and domains. It investigates the challenges posed by non-convex optimization landscapes and the potential pitfalls associated with over fittinghy per parameters to specific datasets.
Optimized Data Selection for Nonlinear Filtering

Nonlinear estimation is ubiquitous in control and signal processing. It aims to estimate the most probable state of a system and its range of uncertainty by fusing data from multiple sensors over time using a filter. Although many efficient filters exist in the literature, their computational cost may increase when the set of data to process is significant. Besides, some data can be redundant or bring little information to the estimation of the state. In that case, their processing is costly and does not contribute to the filter's estimation. This paper introduces a semi-heuristic method to select a relevant subset of observations from a more extensive set of available data, using a cost function based on the approximation of the Cramer-Rao Lower Bound. This approach is adopted on an angles-only optimal navigation scenario where an extensive signal set is available. The results show that the filter achieves close-to-optimal accuracy for a lower computational cost.
Robot Assist Sign Language Recognition for Hearing Impaired Persons Using Deep Learning

The adoption of Sign Language Communication (SLC) systems has become more significant in closing the interaction between the deaf society and the world of hearing people. In this study, researchers aim to contribute to this field by developing a system that helps sign language users communicate through BERT approaches based on deep learning frameworks as well as NLP. Accurate recognition of sign language is critical for SLC systems to work well. Deep learning models are effective in identifying sign language with high accuracy. This study aims to determine the most suitable DL model for identifying sign language and assess the impact of incorporating Natural Language Processing (NLP) techniques in generating frequent and accurate responses in SLC systems. The NLP model will be developed as an optimum return mechanism to generate frequent responses. This research includes testing three different deep learning models: MLP, CNN, and RestNet50v2 to recognize sign language gestures. Restnet50v2 outscored the other two approaches with a 0.97% perfection. As said earlier, the system also generates automated responses using the NLP BERT model, with an overall accuracy of 0.8% and a BLEU score of 0.83%. This method has a way to enhance interaction among the deaf community via the use of technology, opening new avenues for developing intelligent chatbots that can better understand nonverbal communication. Further research can be done to expand its functionality to recognize a broader range of sign language gestures and improve the user interface. Overall, this study demonstrates how technology can enhance the ways of people with deafness or hearing loss by addressing communication barriers.
Evaluation of Deep Learning Approaches for Sentiment Analysis

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.
Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review

Test-Driven Development (TDD) is a methodology in software development that necessitates tests to be written before to the production code. This approach can be used in any software development paradigm that involves writing code, including Agile, Scrum, XP, and Lean. This research paper surveys the impact of TDD on software development with a specific focus on its effects on code coverage, productivity, internal and external software quality, and the affective reactions associated with TDD. The paper also identifies potential challenges and drawbacks of implementing TDD, such as increased overhead and time consumption, a learning curve for developers, and difficulty in testing certain types of code. The studies’ results suggest that TDD can improve code coverage, and code quality, reduce defects, increase productivity and developer satisfaction, improve internal and external software quality, and ultimately lead to higher customer satisfaction.
A Study Towards Exploring Access Control Mechanisms and its Limitations in Cloud Computing

Cloud computing technologies are growing fast day by day. Cloud technologies are attracting enterprises to themselves by providing great and enhanced services. There is no doubt that cloud technologies reduced the burden of the digital world by giving manageable computing services, huge room for unlimited data storage, on-demand software services, great platforms, and access control management systems. To use cloud-based manageable services users and organizations must have access to the cloud. Before using any access control mechanism, the organizations should know about the limitations of the access control mechanism. At present, many access control mechanisms are available in cloud computing. In this paper, our main goal is to identify the access control mechanisms in cloud computing and their limitations in cloud computing.
Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms

Millions of web pages carrying massive amounts of data make up the World Wide Web. Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.
Phraseological Units Containing Similes in Andrić's Devil’s Yard and Their Italian Equivalents

A simile is a semantic-stylistic figure, very common in literary texts and authors tend to use it to give their text a new, broader, figurative meaning. In the category of simile, there exist simile phrases, that is, a special type of phraseological unit intended to create a more powerful artistic impression. Given the high frequency of use of these lexical elements in Ivo Andrić's literary works, to conclude their structure and use in Bosnian, we will analyze several phraseological units that contain similes, all taken from Andrić's novel Devil’s Yard. We consider it interesting to observe these lexical units contrastively, so in the Italian translation of Devil’s Yard (La Corte del Diavolo) done by Lionello Costantini, we will identify the Italian equivalents of original simile phrases and analyze their characteristics. With this contrastive analysis, we aim to offer an overview of the basic features of special lexical constructions which contain similes, and to make generalizations about the similarities and differences in their structure, meaning and use in the two language systems. The results of this study will also allow us to comment on the intensity of the author's creativity and the skill of the translator to recognize the semantic and stylistic value of these phraseological units and transfer them into the Italian language.
On the Sources of Spoken (Italian) Language in a Historical Perspective

In this paper, we research the importance of corpora in historical pragmatics. From the origins of historical pragmatics as a science the question of choosing an appropriate corpus has been in the center of attention. The problematics of historical pragmatics demand a corpus that reflects faithfully the spoken language. However, such registered sources do not exist. Thus, researchers face problems when choosing corpora that have to be written but also reflect speech. That is why we synthesize a review of often-used corpora and we present their advantages and disadvantages. We apply the theory of Giovanni Nencioni (1976) that differs between the spoken language in the true meaning of the word (parlato parlato), written spoken language (parlato scritto), and recited spoken language (parlato recitato). Based on his research, we can claim that some of the privileged forms in historical pragmatics are literary works, theater pieces, religious scriptures, and speech transcripts. Thus, we examine the advantages and disadvantages of the aforementioned corpora and we aim to determine if they are suitable to be used in historical pragmatics. The research shows that it is impossible to find a completely adequate corpus because none of them reflect the spoken language in all of its characteristics and they approach it only partially.
On Translating Some Culture-specific Elements of Oriental Origin in Italian Translations of the Novel Na Drini Ćuprija by Ivo Andrić

Na Drini ćuprija (The Bridge on the Drina) is one of the most recognized and most popular novels by Nobel laureate Ivo Andrić. It has been translated into Italian twice – by Bruno Meriggi (Il ponte sulla Drina) in 1960 and by Dunja Badnjević in 2001. This novel is deeply rooted in the cultural and social context of the early 16th to early 20th century Bosnia and Herzegovina. Since the novel describes historical events and figures who marked four centuries in Višegrad and its surroundings (from the bridge construction in 1516 until its partial destruction in 1914), first under the Ottoman, and then under the Austro-Hungarian rule, it is only natural that many words of Oriental origin are a frequent occurrence in the novel. These words permeated the language of the locals at the time, but also trade, economy, and state affairs, and therefore Andrić decided to incorporate them in his writing - to paint an authentic picture of life in Bosnia and Herzegovina during that time. The focus of this paper is to excerpt the cultural realia of Oriental origin that denote state, social, and religious titles and occupations in the novel. The next step is to analyze these instances contrastively and examine their frequency in the original as well as their frequency in both Italian translations. It has been concluded that, since there is a significant spatial and cultural distance present, the identified realia do not exist in the Italian language repertoire and therefore they pose a problem for translators. Additionally, they are often seen as archaisms, historicisms, and regionalisms even in the contemporary Bosnian language because they have been suppressed from the lexical inventory due to different cultural and social changes that occurred over time.
Pedagogical Conditions for the Organization of a Dual System of Education Based on University and School Connections

The got to actualize the state approach of the country's move to an imaginative way of advancement infers noteworthy changes within the framework of higher instruction. Conceptually, this makes it necessary to consider theoretical studies based on domestic and foreign vocational education systems under the influence of comparative pedagogy with the development of historical and forecast conditions in them. To date, potential bosses and instructive educate have come to a agreement that the cutting edge labor showcase needs highly qualified specialists of a modern arrangement, prepared for any challenges that arise before them in an progressively competitive environment. There's a genuine issue of irregularity of interests of generation, colleges and graduates of higher educational institutions with respect to work and career development. There's a crevice between the requirements of generation within the important competencies of college graduates and the offer of competencies that the college shapes in agreement with the state instructive standard. The article discusses the solution to the problems that arose as a result using elements of a dual education system with the determination of continuity between a university and a school in the specialty «Informatics».
Advancements in News Article Classification: Approaches in Machine Learning and Deep Learning across Sports, Entertainment, Politics, Business, and Weather Domains

The classification of news articles is a crucial technology for processing news information, aiding in the organization of information. It is challenging to classify news due to the continuous emergence of news that requires processing. The modern technological era has reshaped traditional lifestyles in various domains. Similarly, the medium of publishing news and events has experienced rapid growth with the advancement of Information Technology. In this research, news article classification is organized into five selected domains: sports, entertainment, politics, business, and weather news. The classification involves both common and uncommon approaches, along with datasets based on Machine Learning and Deep Learning techniques. Furthermore, the evaluation incorporates various metrics such as precision, recall, and accuracy to compare approaches across the selected five news domains with datasets. To narrow the focus, we limited the news categorization to a few domains (sports, entertainment, politics, business, and weather) to facilitate a better understanding of a large amount of data through concise content. We recommend our work to individuals interested in extending and building upon my research over time.
Natural language processing in assistive technologies

Natural language processing expanded in the last 30 years, arising to cover a wide range of applications in various domains, among them assistive technologies, helping people with disabilities due to accidents, aging, or genetic heritance, to enhance their social integration and their daily life. The growing need in assistive techniques has led to the involvement in the usual devices of intelligent modules to improve and facilitate their use, natural language processing attaining nowadays a high applicability in this domain. The paper discusses first basic methodologies involved in natural language processing like speech recognition, speech synthesis and text processing. Some possible applications are then presented with accent on experimental realization of intelligent modules for assistive devices in the laboratory of our university.
An Empirical Evaluation of Data Integrity Algorithm Performance in Non-Relational Document Databases

Non-relational document databases are a type of NoSQL databases, are gaining popularity owing to their flexibility, scalability, and performance. Document databases provide some distinct security issues. One of the most difficult security challenges is that they are frequently built to be extremely accessible. This can make them more vulnerable to assault if suitable security measures are not taken. Users of database can access data with weak authentication or authorization. Another issue is that they store data in a semi-structured or unstructured format. This makes it difficult to create and execute security safeguards. It may be difficult to detect and secure sensitive data that is kept. Document databases are certain data integrity problems. In this research work, we have developed Big Document DBCrypto’s architecture under which Middle ware provides users to use symmetric cryptography with any hashing algorithm or asymmetric cryptography with digital signatures to ensure data integrity.
Adaptive Numerical Regularization for Variational Denoising Model with Complementary Approach

Denoising is a process to suppress the noise and preserve the important information in the image. In this paper, a complementary approach is proposed for variational denoising problem. A FEM (Finite Element Method) based post optimization method (mesh refinement strategy) is designed which is based on a priori estimate called mean square error. The post optimization algorithm is adaptive and intelligent in nature which allows the adaptive choice of the regularization parameters. The manual choice of the smoothing parameters is taken uniformly on spatial domain and testing of the automatic selection of these parameters in adaptive way. This is an interesting idea of computation. The intelligent and automatic choice of the values for the smoothing function is smaller in the less regular regions of the image, to refine the grid and keep constant in the other complementary regions is one of the main interests, which produces the better and enhanced version of the noisy image. The obtained results have been compared to some other methods.
Classification of Call Transcriptions

Multi-labeled call transcription classiﬁcation is essential for public and private sector organizations, as they spend a lot of time and workforce manually classifying phone call queries. Implementing a machine learning-based auto classiﬁer can effectively assist in this task, especially by reducing the time and resources required. Thepaper proposes an eﬃcient call transcription classiﬁer that not only reduces manpower but also saves time signiﬁcantly. The ﬁrst step in transcript cleaning involves several essential processes, such as converting the transcript to lowercase, applying word embedding techniques, and removing numbers, punctuation, and stopwords. The second step involves designing the model to incorporate four separate classiﬁers, each trainedindependently. Each classiﬁer consists of a bi-directional LSTM layer, an embedding layer, and three subsequent dense layers. These dense layers use the ReLU as an activation function, and softmax as a ﬁnal layer. The experimental results demonstrate that all four classiﬁers have achieved precision, recall, and F1-score greater than 80%. In conclusion, we conduct a comparative analysis of the results against existing studies, demonstratingthat our model has exhibited superior performance.
Identification and Classification of Extremist by Topic Modeling Sentiment Analysis

Social Media forums were formerly seen as the promised land for amusement, education, and boosting purposes that energize the evolution of text analysis to acknowledge the complications of real world. A seedbed for toxic conduct, fanatical content, and political propaganda, they have now become a sump. YouTube is one of the finest platforms where millions of comments thrown per day which consists of valuable and misleading information. Comments may contain slang, foreign and misspelled words that’s very laborious to perform natural language processing (NLP) with those informal languages. Most preceding research diffused the analysis of gigantic unstructured data that comes in different formats and not simply classify in current databases. This study aims to analyze dynamic textual data from randomly selected channels on YouTube. For this ambition YouTube information API v3 was used to scrape a variety of data from YouTube videos. We investigated a few hot topics that are having mordant remarks about racism, LGBTQ, sub teen life aspect, and women and girls are just some of the areas where bullying is ordinary. In this study we summarize a custom dataset of 50 video’s comments related to rap songs and other social events to perform topic modelling sentiment analysis that dwindle the cost of data labeling and annotating. The main objective of our research is to identify and classify each word with their probability from the dataset by applying model hyper parameters alpha, beta, gamma and examine the topic coherence score to measure the semantic similarity of words from extracted topics. This study’s experimental results reveal that the analysis was able to achieve significant sentiment analysis efficiency at both the document and word levels.