Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Panoramic Dental X-Ray Images Using Discrete Wavelet Transform and Unbiased Risk Estimation

The images acquired in any modality will tend to have imposed noise in it. Denoising has to be done as a major step to perform further analysis. The denoising technique employed should be capable of removing all the noises present in the image. The dental panoramic X-ray images are denoised using discrete wavelet transform (DWT) and orthonormal wavelet transform (OWT) with Stein's unbiased risk estimator (SURE). The performance analysis is done based on mean squared error (MSE) and peak signal-to-noise ratio (PSNR) values obtained from both the techniques. The denoising process preserves the information present in the signal like edges of the diseased signal. The denoised image is subjected to feature extraction and thresholding process to extract the diseased portion of the teeth. Artificial neural network is built to classify the images as normal or diseased teeth based on the extracted features. The performance analysis shows that orthonormal wavelet transform with Stein's unbiased risk estimator performs well with good PSNR and MSE value.
A Comparative Study on Challenges and Solutions on Hand Gesture Recognition

Any human–computer interaction application needs to be able to recognize gestures. Hand gesture detection systems that recognize gestures in real time can improve human–computer interaction by making it more intuitive and natural. Color gloves and skin color detection are two prominent hand segmentation and detection technologies, but each has its own set of benefits and drawbacks. For physically challenged people, gesture identification is a crucial technique of sharing information. Support Vector Machine algorithm with Principal Component Analysis, hidden Markov model, superposed network with multiple restricted Boltzmann machines, Growing Neural Gas algorithm, convolutional neural network, double-channel convolutional neural network, artificial neural network, and linear Support Vector Machine with gesture dataset match the interaction of gestures for various postures in real time. Although this method can recognize a huge number of gestures, it does have certain downsides, such as missing movements due to the accuracy of the categorization algorithms. Furthermore, matching the vast dataset takes a longer time. The main contribution of this work lies in a conceptual framework based on the findings of a systematic literature review that provides fruitful implications based on recent research findings and insights that can be used to direct and initiate future research initiatives in the field of hand gesture recognition and artificial intelligence. As a result, a novel study based on a hybrid Recurrent Neural Network (RNN) with Chaos game optimization may reduce classification mistakes, increase stability, maximize resilience, and efficiently use the hand gestures’ recognition system.
Gate-Enhanced Multi-domain Dialog State Tracking for Task-Oriented Dialogue Systems

Dialogue state tracking (DST) task is a core component of task-oriented dialogue systems. Recently, several open-vocabulary-based models were proposed in multi-domain setting, which relies on copy mechanism and slot gate classification modules. However, as the ontology gets more and more complex, it becomes challenging to fill values to slots with type-specific features fully utilized, apply appropriate operation, and tackle the over-trivial carryover problem which used to be neglected. To address the above issues, a hierarchical gate-enhanced DST framework called DSA-Gate DST is proposed in this paper. Domain activity prediction and semantic confirming recognition modules are introduced to track slots from different domains discriminately. Experiment results on multi-domain task-oriented dialog corpora are conducted to show that our model outperforms various baseline algorithms in widely various language settings. Meanwhile, we conduct a comprehensive analysis on the noisy annotation in the MultiWoZ dataset from multiple aspects to explore the potential reasons which limiting DST’s performance.
Application of Augmented Reality for the Monitoring of Parameters of Industrial Instruments

The development of this augmented reality application on mobile devices with Android operating system is oriented for the identification and visualization of industrial equipment and instruments, the interaction through the characteristics and labels of its parts, exploded views and assembly that make up a process of a level control panel. The generation of this mobile application was done with the use of recognition of each element through the generation of a QR code, to be subsequently focused on 3D models using CAD software linked through a multiplatform. Additionally, the simulation and visualization of data from industrial sensors in real time of the process are based on the operation of the instruments, and these generated data contribute to the development of skills for the manipulation and control of industrial processes.
Sentence Pair Augmentation Approach for Grammatical Error Correction

The deep learning model requires a large amount of data when learning a task. The sentence proofreading task requires the text before and after proofreading as the training data. Usually, most of the available publications are after proofreading and are easily accessible. However, the text before proofreading is rarely seen in the general publications and is highly difficult to obtain. In this study, we assume a case where we cannot prepare sufficient amounts of data for training a sentence proofreading task, such as work procedure manuals. We propose a method that automatically generates both pre- and post-proofreading sentences. We generate pseudo post-proofread sentences by Markov chains or GPT-3. The sentences generated by Markov chains are often semantically incorrect. We identify and remove these incorrect sentences by gated recurrent unit (GRU). Then, we generate pseudo pre-proofread sentences by adding noises to the pseudo post-proofread sentences using three different methods. In the experiments, we have compared the case where seq2seq-based grammatical error correction method is trained with a small corpus only, and the case where it is trained with the pseudo-sentence pairs generated in this study in addition to the small corpus. As a result, one of our methods improved the accuracy by 12.1% in the metric BLEU.
Multi-label Classification Using RetinaNet Model

Object detection and classification have several versions which include image classification, object localization within an image, to multiple object detection and localization. Object recognition or classification has moved further to multi-label classification of an image and of an object. In present state of art models, multi-label image classification or a single label for object localization in images is available. Multiple labels for object classification and localization are not available. We present a framework accomplished by the study and customization of the RetinaNet architecture, to do object classification and localization with multiple labels for multiple classes of objects.
A Survey on Web 3.0 Security Issues and Financial Supply Chain Risk Based on Neural Networks and Blockchain

With the development of Web 3.0, issues of privacy and ownership have arisen. Supply chain finance (SCF) has given rise to a risk of credit for mid-cap and small-cap businesses. Regulating the risk involved is a major assignment, and it should be minimized as it is applicable in all the fields of finance in most situations. Web 3.0 privacy and ownership concerns are to be addressed for its advancement. This study focuses on Web 3.0 security issues and SCF and uses a fuzzy neural network (FNN) and blockchain to study the risk of credit for small-cap and mid-cap businesses. The FNN algorithm is devised to appraise the risk involved in SCF and develop the supply chain’s efficiency.
Efficient Image Retrieval Technique with Local Edge Binary Pattern Using Combined Color and Texture Features

Image proceeding is foundation for pattern recognition and machine learning. The recent research focuses on image retrieval techniques with machine and deep learning methodologies. This paper recommends an other approach for extracting images based on combined characteristics of color and texture with edge binary pattern technique. The suggested descriptor first translates an RGB image into HSV color space. In this approach, the HSV color model makes use of the fundamental properties of color image as color, intensity, and brightness of an image. The hue (H) and saturation (S) components support to extract the color features, and the value component is more feasible to obtain the texture features; upon the value constituent of each image, local maximum edge binary patterns (LMEBPs) are used to find the relations among the pixels for every 3 × 3 matrix in order to extract texture features. Finally, all three histograms were used to extract the feature vector. The presented algorithm is experimented on two well-known color databases, Corel-10k and MIT-Visex. The comparative analysis of the proposed approach in terms of retrieval performance, with existing methods like CS-LBP, LEPSEG, LEPINV, has shown a substantial perfection in terms of precision and recall.
Anomaly Detection in Blockchain Using Machine Learning

With the increasing use of Blockchain, community has become increasingly worried about its security, that has led to substantial research by academics, with anomaly detection being a major issue. Regardless of the fact that they can provide availability and integrity, the bulk of public Blockchain systems are decentralized and have minimal confidentiality. The Blockchain network is vulnerable to transaction privacy breaches since all of the network's keys are exposed to everyone. Various security flaws in Ethereum and smart contracts have recently been discovered. As a result, it's critical to improve Blockchain’s security features. This research will mainly use the literature survey method and the inductive analysis method to analyze the relevant research works for anomaly detection in Blockchain technology and to find out the trends and characteristics of the development and application of the anomaly detection models and explore its feasibility in ensuring security in Blockchain technology. This paper also proposes a framework for anomaly detection in Blockchain technology.
Mathematical Proof of Path Independence of J-Integral

Subject Condensed Matter Physics Collection: Oxford Scholarship Online
The Digitization of Onboarding Processes of New Joiners Aimed at Enhancing Soft and Hard Skills

Through the analysis of the process of insertion and training of young graduates in a financial business context, the study highlights the unexpected effects of professional training based on technological tools. The contribution illustrates how the digital skills of recent graduates within a complex organization can respond to training needs not only related to technical skills but also to transversal skills suitable for enabling a path for the integration of young people based on creative skills and collaborative attitudes.
In-Person/Online Attendance at an Educational Technology Workshop. A Study on Student Teachers’ Perceptions at UniBg

This work presents the first phase of a descriptive study, realized at the University of Bergamo, which involved 102 student teachers participating in the “Educational Technology Workshop” of the degree course in Primary Education Sciences. The research objectives were: a. to describe student teachers’ perception of the training experience gained in person or online during the workshop; b. to verify whether the way in which the students attended—face to face/online—influenced their perception of teaching with respect to strengthening their digital skills. The data were collected through an “ad hoc” questionnaire on student teachers’ perceptions and were analyzed in a descriptive and correlational way. The survey revealed that the student teachers’ perceptions regarding the teaching methods of the workshop changed according to the way in which they participated—those attending face to face understood the alternation between lessons and exercises better than those attending online, who, conversely, grasped the transmissive aspect of the workshop meetings better. It also revealed that the method of participation did not seem to have influenced prospective teachers’ perception of their own digital skills. In general, university students’ positive perception of training activities adapted online after the COVID pandemic was confirmed, and it was discovered that future teachers attending online are slightly more sensitive to the teaching methods through which the workshop is carried out and the integration of the workshop with the other training activities of the entire degree course.
The Inclusive Design of a Learning Path with Integrated Digital Teaching in the Education Science Degree Course at the University of Genoa

The contribution presents the results of an exploratory survey conducted with 135 students attending the courses of Special Pedagogy (92 students) and Inclusive Didactics (43 students). The courses were in the Degree Course in Education Sciences of the University of Genoa in the academic year 2021–2022. The aim of the survey was to measure: how the inclusive design, of these two courses, influences the personalization of the learning path and it increases the process of self-regulation and self-efficacy of students. These paths have developed through the application of face-to-face teaching, but during the lessons these courses were enriched by paths on digital platform. The improvement of above-mentioned two aspects allows a better reworking of learning content. Two questions were used to collect students’ points of view: the Self-regulated Knowledge Scale University (SRKS-U) [1, 2], in Italian Version Sara-U [2], and the Perceived School Self-Efficacy Scale [3, 29] adapted and integrated with requests for explanation of students’ choices. The results obtained from the analysis of the questionnaires are encouraging and invite teachers to reflect on the possibility of applying mixed teaching methods within their teachings on a daily basis.
Self-efficacy and Digital Skill of University Teachers

The intertwining between education and technology has led to a groundbreaking rethinking of the role of university education to prepare students to their future integration in the 21st century knowledge society, rooted in networked teaching, learning, and working environments, and in technological contexts, requiring embedded multimedia digital skills and literacy. Knowledge development is nowadays narrowly related to the enhancement of 21st century skills in agreement with 21st century digital skills. The re-designing of teaching practices for the strengthening of digital skills is becoming a key point of discussion, because less than 50% of teachers often uses technology in class. On the other hand, digital students constantly use technology but the idea that they can be considered highly competent in technology – without training – is nowadays seen as a myth. Both teachers and students may lack digital competence. Digital competence means information and communication technology skills; if applied to teachers, it refers to technical skills but it also implies integrating technological devices and digital resources in educational contexts to reach desired learning goals. In this research it is analyzed the role played by teachers in implementing technology into instruction to enhance students’ digital skills. The research is focused on context of University Federico II of Naples. To explore the levels of teachers’ technology acceptance in a university context, this research has been carried out with a volunteer sample of 56 university professors from the Department of Humanities of the University Federico II of Naples.
Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees

Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and the educational scenario is not an exception. However, the usage of the so-called explainable Artificial Intelligence, even if desirable, is still limited, especially whenever we consider educational datasets. Moreover, the time dimension is not often regarded enough when analyzing such types of data. In this paper, we have applied the fuzzy version of the Hoeffding Decision Tree to an educational dataset, considering separately STEM and Social Sciences subjects, in order to take into consideration both the time evolution of the educational process and the possible interpretability of the final results. The considered models resulted to be successful in discriminating the passing or failing of exams at the end of consecutive semesters on the part of students. Moreover, Fuzzy Hoeffding Decision Tree occurred to be much more compact and interpretable compared to the traditional Hoeffding Decision Tree.
Two years of Blended Synchronous Mode Learning in Higher Education. The Students’ Experience Told Through Their Metaphors

Both the documents produced by the main international organizations and the thematic scientific literature highlight the impact of distance learning and blended synchronous learning, introduced with the COVID-19 pandemic. Two years later, what seemed to be an exceptional initiative is turning into an educational opportunity for students and teachers, provided that reflection on these impacts and results helps to inform some changes in the teaching-learning act as a “lesson learned”. A qualitative analysis on students’ metaphors and representations of their biennial experience in distance learning and blended synchronous learning in two university courses is here presented. In line with the findings of similar studies, possible hints are given to improve the teaching-learning relationship when distance and blended synchronous mode coexist.
DLP: towards active defense against backdoor attacks with decoupled learning process

Abstract Deep learning models are well known to be susceptible to backdoor attack, where the attacker only needs to provide a tampered dataset on which the triggers are injected. Models trained on the dataset will passively implant the backdoor, and triggers on the input can mislead the models during testing. Our study shows that the model shows different learning behaviors in clean and poisoned subsets during training. Based on this observation, we propose a general training pipeline to defend against backdoor attacks actively. Benign models can be trained from the unreliable dataset by decoupling the learning process into three stages, i.e., supervised learning, active unlearning, and active semi-supervised fine-tuning. The effectiveness of our approach has been shown in numerous experiments across various backdoor attacks and datasets.
A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

Abstract Feature selection is a knowledge discovery tool to understand the problem by analysing features. In particular, the application of feature selection in data mining can not only improve the quality of extracted patterns and knowledge but also decrease computational costs. Various techniques have been applied to this complex optimization problem, in which metaheuristics have been validated to be superior. This study introduces a new metaheuristic known for having lean and fast programming, inspired by the sunflower's motions for feature selection for the first time. It is equipped with a v ‐shaped transfer function and associated with the KNN classifier to become the binary sunflower optimization (BSFO). A total of 12 variants of BSFO are designed based on the chaos theory and Lévy flights, called improved binary sunflower optimization (IBSFO). A discussion between these improvement theories for feature selection has also not been made yet, and it is performed in this paper using 15 benchmark datasets from the UCI repository. The experimental results show that all variants can advance the fitness value of BSFO, and nine of them considerably decrease the computational costs. Furthermore, the chaotic BSFO with the Chebyshev function, taking replacement to normal rand, has the lowest fitness value (−11.37%) and execution time (−9.31%) than the original BSFO. Further, IBSFO is compared with another eight metaheuristics and outperforms these competitors on average fitness value and execution time. Overall, IBSFO proved to find subsets with reduced dimension and high accuracy with meagre computational cost due to its robust explorative and exploitative capacities.
改良型先行インディケータを用いた非定常時系列の変化方向予測法

This paper presents a method for forecasting the change direction of nonstationary time series using the improved leading indicator. The leading indicator is a method developed by Ehlers that translates a time series mathematically in the future direction with respect to the time axis and calculates the leading value of the time series. However, this method has the problem that the leading value can be calculated only in the low frequency region with a normalized frequency of 0.06 or more (normalized period of 17 or more) at the maximum. In order to solve this problem, by gentle slope the amplitude characteristics in the low frequency region of the leading indicator, it is possible to calculate the leading value in the frequency domain with a normalized frequency of 0.25 or more (normalized period of 4 or more) at the maximum. By applying the preceding value to the instantaneous periodic time series by the improved sine wave indicator developed by Ehlers, it is possible to forecast the change direction of the non-stationary time series in the short term.
The Evaluation of Syllable-Based Language Model in Malay and Indonesian Speech Recognition

Malay and Indonesian language are both syllable-friendly languages. It is convenient to covert Malay and Indonesian words to syllables and the syllable-based language model (LM) can be applied into actual speech recognition systems. This paper proposed a method and the algorithm for the syllabification of Malay and Indonesian words. In order to evaluate the perplexity (PPL) of syllable-based LM, this paper used SRILM to compare the PPL between word-based, syllable-based, phoneme-based and character-based [Formula: see text]-gram LMs. The experimental results show that the perplexity of the syllable-based LM is better than the word-based model, while the perplexity of the character-based or phoneme-based LM is better than the syllable-based LM. However, when the syllable-based LM is applied into actual speech recognition systems, it has the advantages of lower cost, similar performance and easier deployment, compared with the speech recognition systems using deep learning models.