Reviewerly/exHarmony huggingface dataset:
Multi-Modal Medical Image Denoising using Wavelets: A Comparative Study

Introduction Medical imaging is an imaging of various body parts. It includes radiology, thermograph, endoscope, medical photography, and microscopy. 1 It plays a main part for diagnosis, research related findings etc. These images are generally get noisy while acquisition and transmission of th
Adaptive Type-1 Fuzzy Controller for Lag-Dominant First and Second Order Nonlinear Systems

Most of the current day industries are suffering from nonlinear processes. Thus, both the stability and the process performance of high-degree nonlinear systems with dominating delay might be difficult to achieve. Adaptive and intelligent fuzzy classifiers and controllers have been more popular in recent years as a means of overcoming a significant number of difficulties faced by the industrial sector. A large number of dynamic process plants with a variety of orders and kinds have been represented heuristically and recognized. Fuzzy structures have also been employed for these interactive systems by making use of fuzzy and linguistic techniques. In view of all these initiatives, the purpose of this paper is to conduct an experimental investigation into the performance of a LabVIEW-based Type-1 Adaptive Mamdani Fuzzy Controller (AMFC) that has been designed and applied over a lag-dominant and a second-order nonlinear Dual Input Tank System (DITS) and Single Input Tank System (SITS). Compared to other type-1 approaches that were previously experimented with and are now in existence, the adaptability of AMFC demonstrates that it is quite effective. Performance indices such as the Integrated/Summated Absolute Error (IAE) and the Integrated/Summated Squared Error (ISE) are also computed for several variable set point profiles of DITS. These indices measure errors in integrated absolute value and integrated squared value, respectively. Adaptive Type-1 Intelligent Fuzzy Controller’s response and error reduction efficiency have been found for several flow configurations of DITS, namely Multiple Input Multiple Output (MIMO) and Single Input Single Output (SISO). From the results, it can be concluded that the proposed experimental validation may be used for a wide variety of process challenges that are experienced in industrial systems to achieve robust and low error controller performances.
Pattern recognition and signal parameters extraction using machine learning methods

Abstract Machine learning methods can be used for signal processing in different cases of physics research. A convolutional neural network was developed for the task of pulse counting in particle detectors for high energy physics. For the extraction of signal parameters was used a network with convolutional autoencoder architecture and a subsequent result reconstruction algorithm was developed and applied. A convolutional neural network was also developed for seismic studies with the task of identifying different events in seismograms. All of the algorithms with their architecture, input and output are presented and discussed.
Tableaux approach for contact logics interpreted over intervals

Contact logics are modal logic that is developed for reasoning about region-based theories of space. We develope a tableaux approach for contact logics interpreted over intervals (CLIOI) on the reals. For obtaining sound and complete tableaux-based decision procedures, the main technical tool is the semantic tableaux approach. We use intensively the following concepts: tableaux methods, termination of tableaux methods, saturated tableaux, termination theorem, soundness theorem, truth lemma, and completeness theorem.
Investigating Students’ Speaking Difficulties and Finding Alternative Solutions at Islamic Affiliated Junior High Schools

In the realm of English for Specific Purposes (ESP), the cultivation of speaking skills demands specialized learning techniques and models. This particular study embarked on the mission of tackling the speaking challenges encountered by students in Islamic-affiliated junior high schools in West Lombok, Indonesia. Employing a qualitative approach, the researchers harnessed interviews and observations as their research methods. The investigation uncovered a spectrum of hurdles faced by students, including a restricted vocabulary, a dearth of fluency, and an apprehension of making mistakes. In response to these challenges, the researchers strategically employed interviews to augment vocabulary and speaking proficiency, leveraging humor and jokes to foster a relaxed and enjoyable learning atmosphere. The outcomes were promising, showcasing heightened engagement and motivation among students to actively participate in speaking exercises during lessons. The discernible improvement in the students' speaking skills was multifaceted, encompassing advancements in vocabulary, fluency, and overall confidence. Notably, the students not only demonstrated progress but also exhibited positive attitudes towards the teaching methodology employed, describing it as both enjoyable and effective. This study underscores the efficacy of integrating interviews and humor as potent tools in addressing speaking difficulties. It underscores the paramount importance of crafting an engaging and pleasurable learning milieu, emphasizing that such an environment contributes significantly to the enhancement of students' speaking skills. Ultimately, this research sheds light on the transformative impact of innovative teaching approaches within the realm of ESP, offering a compelling case for the adoption of these strategies to foster improved speaking abilities among students.
Overview: How Does the Paper "The Continuity of Prime Numbers Can Lead to the Continuity of Even Numbers" Relate to the Goldbach Conjecture

The paper "The continuity of prime numbers can lead to the continuity of even numbers" proposes three clever methods to prove the Goldbach conjecture from another path that humans never anticipated and was also the correct path. "The continuity of prime numbers can lead to the continuity of even numbers" A preprint has previously been published (Xie ling. 2021).
Information Technology Enablement of Indian Police Department

Application of Information Technology in the department of police is surely an idea that will be would help in their primarytask, which is about to control and prevent crime in society. It would also help the police department to create a better andmore intelligent system for the detection of crime in advance and to investigate it. The applications of IT would also help thepolice department to optimize its efforts, personnel, and resources.The police department is established to maintain a secure environment with disciplinary approaches and well-definedtraditional parameters. But with the advancement of new cyber threats, the necessity for the IT sector in the police departmenthas increased. Information technology influenced the whole spectrum of society, each corner of the educational field, andtrading services to a great extent which enhance the need for skill in every aspect of opportunities for development.Information Technology refers to the use application of computers to analyze, store and transmit valuable information,viewing the part of the world into a single entity of the market. Its major role in the education system has introduced abreakthrough in acknowledgment areas and produced a variety of opportunities. The major objective of using informationtechnology in the police department is to determine and describe the role of IT for better achievements in the area of Safetyand Security by enabling it to the police department. There is a requirement for the management of data in a prudent mannerso that it could be used for future studies and prediction of crime. The high-end integrated IT solutions have generated widescope of sustainability which leads to the growth of IT services, where enterprise problem-solving and skilled systemsincorporated, are expected to fuel growth in this segment.
Logic and Sets

Before we venture into the topic of analysis, we need to be sufficiently well-versed with the language of mathematical logic and proofs. The most common misconception among non-mathematicians is that mathematics is all about numbers and equations. Whilst they are important, they are not the main features of mathematics. Mathematicians view numbers as one of the languages or tools to communicate mathematics because they allow us to objectively quantify certain properties, structures, or act as convenient labels. Equations allow mathematician to compare or relate some concepts together.
Multicriteria Optimization of Chemical Reactions Using Interval Analysis. Parallel Scheme for Implementing the Computational Process

An algorithm for determining the permissible interval values of variable parameters in solving the problem of multi-criteria optimization of conditions for a multi-stage chemical process is considered. The mathematical model of kinetics is given in the form of an interval problem, according to the intervals of kinetic parameters, and its solution is carried out by a two-line method. This approach makes it possible to take into account possible fluctuations in the reaction conditions and their influence on the values of the optimality criteria. This is especially important for industrial processes and must be taken into account when modeling laboratory reactions with subsequent introduction into production. The object of research in this work is the catalytic reaction of hydroalumination of olefins in the presence of triisobutyl aluminum, the product of which are higher aluminogranic compounds. The developed kinetic model of the process takes into account possible parameter intervals, due to fluctuations in the reaction temperature. And the solution of the multi-criteria optimization problem makes it possible to take into account the optimal conversion interval of the initial olefin. To assess the adequacy of the solution, a comparison is made with experimental data and according to the criteria for evaluating the quality of Pareto approximation. The algorithm of parallelization of calculations for the task of multi-criteria optimization using interval analysis is proposed.
Development of Components for Monitoring and Control Intelligent Information System

Development of digital economy requires improving of IT personnel training to ensure a more complete interaction of education and industry. In particular, it is necessary to develop the students’ ability to design industrial intellectual information systems. The article analyzes the experience of Lobachevsky State University of Nizhny Novgorod in organizing a series of educational projects for the information support of Sergach Sugar Plant. These projects were aimed at the development of components of an intellectual and information system for monitoring and controlling sugar production. The purpose of the research was to create educational and methodological support of these projects. Such educational support includes the definition of objectives, plans and schedules, the creation of educational resources. The most important educational objective of the projects was the practical learning of the methodology of intellectual information system creating. The project approach was used as the main method of organizing the educational and research work. The considered training experience is of a great importance not only for solving the partial production problem. It can be useful in a general case when students study the methodology of intellectual information system creating. This approach is the important form in the training of IT personnel for the digital economy.
How to Make Lanczos-Montgomery Fast on Modern Supercomputers?

This paper deals with the performance analysis of the INM RAS implementation of the block Lanczos-Montgomery method that was used to accomplish the factorization of the RSA-232 number. Other steps of RSA-232 factorization carried out by CADO-NFS open source library are also mentioned. Further adaptation of parallel block Lanczos-Montgomery method for the modern supercomputers is discussed, including implementation of basic operations of the method on GPUs.
Facial Profile of Young Indian Women from Maharashtra-A Cross-Sectional Pilot Study

Abstract Context: The anthropometric facial clinical proportions are used in the field of orthodontics, maxillofacial and plastic surgery for aesthetic or abnormality corrections. There is lack of enough literature on the facial profiles of Indians. Aim: To assess correlations between facial parameters and stature of young Maharashtrian women by using anthropometry. Settings and Design: It is a cross-sectional observational pilot study at Maharashtra Institute of Dental Sciences &amp; Research, after approval from the Institutional Ethical Committee. Methods and Material: The study included 15 students of 21–23 years age selected by simple randomisation. The facial parameters were measured by sliding vernier calipers after identifying facial landmarks by stickers. Facial height (FH) in thirds; upper FH (UFH), middle FH (MFH) and lower FH (LFH); facial width (FW) and stature or overall height (OH) were calculated to define average facial features. Statistical Analysis: Multiple pairwise statistics and simple linear regression analyses were done for various dependent variables. Results: The means of UFH, MFH, LFH and total facial heights (TFH) were found to be 5.2 ± 0.54, 5.35 ± 0.34, 5.16 ± 0.44 and 15.7 ± 0.98 cm, respectively. The TFH showed a moderate correlation with stature ( P ≤ 0.05, r = 0.64) and a strong correlation with lower lip length ( P = 0.001, r = 0.78). Facial width showed a negative correlation with facial shape ( P ≤ 0.05). Conclusions: The selected sample showed the statistically insignificant difference between UFH, MFH and LFH indicating equitable distribution among Indian women of Maharashtrian origin of 21–23 year age group. Longer TFH is positively correlated with higher stature and longer lower lip length.
Biomedical event extraction using pre-trained SciBERT

Abstract Biomedical event extraction is applied to biomedical texts to obtain a list of events within the biomedical domain. The best GENIA biomedical event extraction research uses sequence labeling techniques with a joint approach, softmax decoder for event trigger identification, and the BioBERT v1.1 encoder. However, this event extraction model has three drawbacks: tasks are carried out independently, it does not provide special handling of multi-label event trigger labels, and it uses an encoder with vocabulary from non-biomedical domains. We propose to use the pipeline approach to provide forward information sigmoid to address multi-label event trigger labels and alternative BERT encoders with vocabulary from the biomedical domain. The experiment showed that the performance of the biomedical event extraction model increased after changing the encoder, which had been built using a biomedical-specific domain vocabulary. Changing the encoder to SciBERT while still using the joint approach and softmax decoder increased the precision by 4.22 points (reaching 69.88) and resulted in an F 1-score of 58.48.
Sentiment analysis method of consumer comment text based on BERT and hierarchical attention in e-commerce big data environment

Abstract This study proposes an emotional analysis method of consumer comment text based on Bidirectional Encoder Representations from Transformers (BERT) and hierarchical attention. First, using the BERT pre-training model, the left and right contextual information is fused to enhance the semantic representation of words and generate dynamic word vectors containing contextual semantics. Second, the bidirectional long short-term memory network is used to obtain the sequence feature matrix, and the sentence representation and the text representation are obtained using the two-layer long short-term memory. Finally, the local attention mechanism and the global attention mechanism are introduced into the sentence representation layer and the text representation layer, respectively, and the text emotion of consumer comments is classified by softmax. Experiments show that the accuracy of the proposed method in Laptop data set is 93.01% and that in Restaurant data set is 92.45%. Therefore, the performance of the proposed method in the emotional analysis of consumer comment text is significantly better than that of the comparison method.
Application of Different Python Libraries for Visualisation of Female Genital Mutilation

Utilizing data visualization facilitates the analysis and comprehension of common data provided by the media, individuals, governments, and other sectors. Python is a well-known programming language that excels at scientific data visualization. This thesis utilizes a variety of Python modules, including Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Bokeh, to illustrate female genital mutilation. The purpose of this thesis is to illustrate female genital mutilation and explain its performance pattern using a complex, interactive diagram that integrates multiple types of Python libraries. In comparison to other libraries, Plotly is the simplest, yet it performs at the highest level. NumPy and Matplotlib are combined to produce Hexbins charts. NumPy provides an N-dimensional plot, and Matplotlib allows for the plot's colours to be customized. Despite its limited customization options, the Seaborn library is suitable for both data visualization and statistical modelling. Due to this deficiency, the Seaborn library is frequently combined with Matplotlib to generate superior visualizations. As a result, this thesis will be recommended to both specialists and novices as worthwhile reading. In addition, it will assist the government in drafting legislation to end female genital mutilation. They will comprehend the significance of combining multiple Python modules to generate intricate interactive diagrams for data visualization in the field of data science. This information will be posted online to contribute to the corpus of knowledge.
Redefining Audience Participation: The Immersive Interaction in “Wolf and Spices VR” and the Enlightenment of Journalism and Communication

In this article, we examine the impact of immersive interactions in “wolf and spice VR” on audience engagement and their impact on messaging. We delve into the immersive interactive elements of film and explore their potential applications in messaging to increase audience engagement and emotion resonance. Immersive interactions and virtual reality technologies serve as innovative information and communication tools that provide viewers with more immersive experiences and increase their participation and attention. The article examines the evolution of audience engagement theory, from the influence of traditional media to the empowerment of digital technology and new media, with a focus on interactive entertainment and audience autonomy, and discusses the application specification of immersive interaction in the practice of information communication, highlighting the potential of immersive Experiences aimed at increasing public partici-pation and attention to reporting. “Wolf and Spice VR” focuses on environment design, character interaction, visual effects, tactile feedback, interactive storytelling, social interaction and other aspects have been developed to strengthen the educational role of immersive interaction in journalism and communication. Finally, the challenges and opportunities of immersive interactive experiences are discussed, highlighting the need for quality content creation, technological advances and audience acceptance creation.
Bridging Human-Computer Interaction and Ecofeminism: Insights from Deleuze and AI

The rapid advancements in Artificial Intelligence (AI) and human-computer interaction (HCI) have profound implications for society. However, the integration of Ecofeminism and Deleuzian philosophy still needs to be explored in this context. While existing literature has separately examined HCI, Ecofeminism, and Deleuze’s concept of “becoming-woman,” there is a lack of interdisciplinary studies that bring these diverse perspectives together, particularly in AI and contemporary literature. This paper introduces a novel framework that integrates HCI, Ecofeminism, and Deleuze’s “becoming-woman” to analyze AI technologies and literary works. We employ this interdisciplinary lens to critique and reimagine AI systems, advocating for more inclusive and ecologically responsible designs. We offer a comprehensive review of contemporary literature that engages with these themes, enriching the discourse in HCI and literary studies. Moreover, we integrate neural machine translation (NMT) models into interactive machine translation to propose a classical prefix-based NMT segment-based interactive protocol, i.e., the interactive NMT (iNMT) method. The positive impacts of such an iNMT method, when viewed through the lens of HCI, ecofeminism, and Deleuzian philosophy, lie in its potential to democratize interactions, amplify marginalized voices, and offer new tools and metaphors for exploring fluid identities in contemporary literature and beyond. Our findings reveal that incorporating Ecofeminist and Deleuzian perspectives can lead to more ethical and inclusive AI systems. Additionally, we identify emerging trends in contemporary literature that resonate with these interdisciplinary concerns, suggesting a fertile ground for future research and collaboration.
A Method for Reducing False Negative Rate in Non-Maximum Suppression of YOLO Using Bounding Box Density

In the previous non-maximum suppression (NMS) in you only look once (YOLO) v5, false negative error happens even when there are many bounding boxes for an object because all bounding boxes have lower confidence score. This work finds that a lot of bounding boxes near an object of false negative error are removed because of low confidence score. This paper proposes a new modified confidence score that is increased when bounding boxes with the same class prediction are located densely. The proposed method reduces the false negative error caused by low confidence score effectively. Experimental results show that the proposed method detects 25.33% more objects than conventional NMS at mAP@0.5.
Machine Learning-Based Work Efficiency Monitoring Method for Student Supervisor

As student supervisor workloads increase, ensuring efficient support is challenging. A novel machine learning method using clustering analysis is proposed to gain insights into time allocation and identify optimization opportunities. Specifically, a covariance distance measure analysis approach is presented for characterizing the closeness of data items in multi-dimensional cluster analysis through efficient distance measuring. The membership degree of each sample point is first determined concerning class characteristics through the fuzzy c-means approach, which involves first assigning weights to data items. Then, the membership degree is used to determine the degree of dissimilarity between the samples. Finally, the covariance distance measurement between sample points is used as the second optimization criterion to distinguish between data types, and the optimal solution is calculated iteratively by alternating fixed variables to optimize the clustering metric and the distance measurement learning parameters and produce more accurate clustering results. The experimental findings on datasets demonstrate that the proposed approach outperforms benchmarks in terms of clustering accuracy. Therefore, the high clustering accuracy empowers the student supervisor to make informed decisions about time allocation, allowing them to optimize their efforts and resources.