Reviewerly/exHarmony huggingface dataset:
Entropy-based Sampling for Streaming learning with Move-to-Data approach on Video

The current paradigm of training deep neural networks relies on large, annotated and representative datasets. They assume a static world where the target domain does not change. However, in the real-world, data changes over time and is often available on the fly. Naive retraining on new data causes catastrophic forgetting and the network is unable to generalize on old data. Streaming learning is a type of incremental learning where networks learn sequentially and as soon as a sample is available from the data stream. Instead of training on every new sample, we propose an uncertainty based selection criteria to improve our previously proposed fast streaming learning method Move-to-Data (MTD), called Entropy-based MTD (EMTD). Besides, streaming learning methods have so far mostly used Convolutional Neural Networks (CNNs) but in recent times Vision Transformers (ViTs) have shown much better performances for many vision tasks. Therefore, we use ViT based Video Transformer to analyse MTD, EMTD and their gradient descent based "retargeting" steps. We have compared the performances of EMTD with MTD (w/wo retargeting) and a popular streaming learning method ExStream for the transformer. EMTD is able to outperform baseline MTD, and EMTD with retargeting achieves close results as ExStream and is ∼ 1.2 times faster.
RAST: Restorable Arbitrary Style Transfer

The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image. Although current methods have shown some success in transferring style, arbitrary style transfer still has several issues, including content leakage. Embedding an artistic style can result in unintended changes to the image content. This paper proposes an iterative framework called Restorable Arbitrary Style Transfer (RAST) to effectively ensure content preservation and mitigate potential alterations to the content information. RAST can transmit both content and style information through multi-restorations and balance the content-style trade-off in stylized images using the image restoration accuracy. To ensure RAST’s effectiveness, we introduce two novel loss functions: multi-restoration loss and style difference loss. We also propose a new quantitative evaluation method to assess content preservation and style embedding performance. Experimental results show that RAST outperforms state-of-the-art methods in generating stylized images that preserve content and embed style accurately.
Adaptive Multi-granularity Aggregation Transformer for Image Captioning

In image captioning, images often contain complex scenes where features at a single granularity level fail to capture all the visual information. For instance, grid features of an image provide spatial details but lack an understanding of semantic objects. Therefore, it is necessary to fuse the multi-granularity features of an image for a comprehensive representation. In this paper, we propose an adaptive multi-granularity aggregation transformer that integrates grid, region and global features of image. In contrast to previous approaches that rely on single-feature or two-feature representation, our approach integrates features of different granularity levels, which overcomes the incompleteness of traditional visual information characterization. Specifically, we construct an encoder with a multi-granularity feature enhancement module that explores intrinsic relationships between different features to reduce the redundancy of feature representation. We also design a multi-granularity feature adaptive fusion module to adjust the attention of features at different scales, enhancing cross-modal inference ability. Experiments on the MSCOCO dataset demonstrate that our model achieves superior performance, with a CIDEr score of 138.6 on the “Karpathy” split, surpassing the state-of-the-art fusion model by 2.5 points.
A multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles

Due to the difficulty of Global Navigation Satellite System (GNSS) in urban canyon environments, GNSS and inertial navigation system (INS) fusion positioning is widely used in the field of intelligent driving. In actual positioning, INS usually uses the vehicle physical model as a constraint to calculate inertial measurement unit (IMU) data to supplement the vehicle position and attitude as GNSS positioning information. However, the existing algorithms usually use a fixed physical model. When the vehicle’s driving behavior changes, such as lane-changing, acceleration, deceleration, etc., the fixed model cannot provide long-term constraints on cumulative errors, resulting in a decrease in fusion positioning accuracy and stability. Therefore, this paper proposes a multimodal fusion positioning method with adaptive driving behavior for intelligent vehicles. First, to adapt to multimodal driving behavior, a multimodal assisted positioning physical model with adaptive driving behavior (MAPM) is established, which expresses different physical models probabilistically based on driving behavior and historical data. Secondly, high-precision estimation of vehicle positioning parameters is achieved based on model probability and interactive multi-model (IMM). Finally, effective fusion of MAPM/GNSS/INS positioning information is achieved under the federated Kalman filtering framework. The simulation results show that compared to traditional GNSS/INS fusion positioning, the proposed method can effectively improve positioning accuracy and stability, especially in driving conditions with changes in driving behavior.
Blended Learning as a Sustainable Strategy in Digital Transformation—A Study of Challenges Faced by Parents at Sultanate of Oman

This paper explores the concept of blended learning as a sustainable strategy in facilitating digital transformation within organizations. It begins by analyzing the driving forces behind the shift toward digitalization and the challenges that necessitate a balanced learning approach. The study delves into the core principles of blended learning, highlighting its adaptability and effectiveness in fostering employee development and organizational learning. The study explores the changing dynamics of parental involvement in the learning process due to blended learning. It examines the shifting responsibilities and roles parents are expected to play in facilitating their children’s education, both inside and outside the traditional classroom.
A study of brains complex organs-organisms with artificial intelligence system to evolve cardinal feature-manifestations of brain`s (self-organizing)

: Embedding carnal (somatic or physical) restraints over the artificial intelligent system (i.e., artificially-intelligent system) in ample the similar way that the ‘human-brain’ must grow, progress plus function in the physically real, tangible and biological constrictions that lets system to advance feature-manifestations of the brains of multifaceted organs and organisms so as to solve brain issues. : Placing carnal restraints on AI-based model-system, i.e., artificially intelligent system. : spatially embedded recurrent neural nets (RNNs), 3D Euclidean space, where message of fundamental neural-cells are hampered by ‘sparse-connectome’ recurrent-neural-nets (RNN). : RNNs converge over anatomical, structural functional features universally originate within primates (cardinal, mandrill), and macaques’ cerebral/rational, brainy-cortices. Explicitly, they congregate/ (converge) over resolving implications via segmental (modular) tiny-world nets, in which functionally analogous-units spatially configure/construct themselves to use the dynamically effective varied-discerning code. Since features occur in union RNNs show how many mutual anatomical, functional-brain patterns (motifs) are deeply linked, can be ascribed to basic biologic optimization-processes. : RNNs merge biophysical limits in AI system plus aid as a bridge amid anatomical functional researchers to move ability neuroscience on.
KyotoMOS: An Automatic MOS Scoring System for Speech Synthesis

The Mean Opinion Score (MOS) serves as a subjective measure for assessing the quality of synthesized speech. Nevertheless, the conventional approach to MOS evaluations can be resource-intensive in terms of both time and cost. This article unveils an automatic MOS scoring toolkit that builds upon our success in securing the top position for some metrics in VoiceMos2022 Challenge and emerging as champions in some tracks of the VoiceMos2023 Challenge. We offer a pre-trained MOS scoring tool for English and provide training code for other languages. Our documentation, examples, and source code are available at https://github.com/superphysics/KyotoMOS.
PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition

Most of the current speech data augmentation methods operate on either the raw waveform or the amplitude spectrum of speech. In this paper, we propose a novel speech data augmentation method called PhasePerturbation that operates dynamically on the phase spectrum of speech. Instead of statically rotating a phase by a constant degree, PhasePerturbation utilizes three dynamic phase spectrum operations, i.e., a randomization operation, a frequency masking operation, and a temporal masking operation, to enhance the diversity of speech data. We conduct experiments on wav2vec2.0 pre-trained ASR models by fine-tuning them with the PhasePerturbation augmented TIMIT corpus. The experimental results demonstrate 10.9% relative reduction in the word error rate (WER) compared with the baseline model fine-tuned without any augmentation operation. Furthermore, the proposed method achieves additional improvements (12.9% and 15.9%) in WER by complementing the Vocal Tract Length Perturbation (VTLP) and the SpecAug, which are both amplitude spectrum-based augmentation methods. The results highlight the capability of PhasePerturbation to improve the current amplitude spectrum-based augmentation methods.
Automatic Labeling of Tibetan Prosodic Boundary Based on Speech Synthesis Tasks

Prosodic is the highest expression of speech dynamics, which is mainly reflected in the pause, tone intensity, accent, and rhythm during natural pronunciation. Prosodic labeling is an important factor in improving the naturalness of speech and enhancing semantic understanding. The extraction of prosodic information can make the effect of speech synthesis closer to nature. In this paper, from the theory of Tibetan grammar and the characteristics of Tibetan speech, we design a method for automatic labeling of Prosodic boundaries that includes Tibetan text, acoustic features, and other Tibetan speech characteristics of Tibetan speech around the task of Tibetan speech synthesis. We choose 20975 Tibetan speech synthesis corpus to validate the designed automatic labeling method. The F1 values of Prosodic words, Prosodic phrases, and intonation phrases obtained by using the Prosodic labeling rule are 95%, 93.4%, and 90.4%, respectively, which provide certain feasibility and scientificity for mining the regular features of the pronunciation of the Tibetan language in the task of Tibetan speech synthesis.
Research on the classification method of knowledge question intention for Tibetan language curriculum

As an important part of the intelligent question-answering system, intention classification has attracted more and more researchers ’ attention. Tibetan corpus is different from other common corpora. Its grammatical structure is relatively complex, and there is no large amount of labeled data, which makes the research of intention classification in this field scarce. Given the above problems, this paper takes Tibetan curriculum knowledge as an example and introduces the ZangBert-BiLSTM modeling question intention classification task. Firstly, ZangBert encodes the semantic information in the text sequence. In modeling the semantic information, the function word information is introduced by inserting special flag bits into the sequence. Then, the BiLSTM model is used to model the long-distance dependence information in the semantic information sequence. Finally, these text features are sent to the downstream classifier for intention classification. In order to verify the validity of the question intention classification model of primary school Tibetan curriculum knowledge, the corresponding question intention classification data set is labeled, and good intention classification performance is obtained on the data set, which verifies the validity of the work done in this paper.
A Large Vocabulary End-to-End Myanmar Automatic Speech Recognition

In recent years, sequence-to-sequence technology has become popular in automatic speech recognition area. This model replaces the classic complex pipeline with a single neural network architecture. This paper proposes the use of transformer- and conformer-based models on Myanmar automatic speech recognition system (UCSY-Myan-ASR). Classical hybrid long short-term memory (LSTM) and end-to-end models are presented and evaluated to improve error rates. The experiments were carried on the UCSY-82-hour speech corpus and evaluated in terms of syllable error rate (SER) and character error rate (CER). Using the Transformer approach, the best performance in the daily conversation domain reaches the SER of 9.6% and CER of 7.3%. When using the conformer model, the best performance in the news domain is 10.6% SER and 6.9% CER respectively.
Interpretable extractive text summarization with meta-learning and BI-LSTM: A study of meta learning and explainability techniques

Text summarization is a widely-researched problem among scholars in the field of natural language processing. Multiple techniques have been proposed to help tackle this problem, yet some of these methodologies may still exhibit limitations such as the requirements for large training datasets, which might not always be possible, but more importantly, the lack of interpretability or transparency of the model. In this paper, we propose using meta-learning algorithm to train a deep learning model for extractive text summarization and then using various explainatory techniques such as SHAP (Shapley, 1953), linear regression (Lederer, 2022), decision trees (Fürnkranz, 2010), and input modification to gain insights into the model’s decision making process. To evaluate the effectiveness of our approach, we will compare it to other popular natural language processing models like BERT (Miller, 2019) or XLNET (Yang et al., 2020) using the ROUGE metrics (Lin, 2004). Overall, our proposed approach provides a promising solution to the limitations of existing methods and a framework for improving the explainability of deep learning models in other natural language processing tasks.
Assessing second-language academic writing: AI vs. Human raters

The quality of writing in a second language (L2) is one of the indicators of the level of proficiency for many college students to be eligible for departmental studies. Although certain software programs, such as Intelligent Essay Assessor or IntelliMetric, have been introduced to evaluate second-language writing quality, an overall assessment of writing proficiency is still largely achieved through trained human raters. The question that needs to be addressed today is whether generative artificial intelligence (AI) algorithms of large language models (LLMs) could facilitate and possibly replace human raters when it comes to the burdensome task of assessing student-written academic work. For this purpose, first-year college students (n=43) were given a paragraph writing task which was evaluated through the same writing criteria introduced to the generative pre-trained transformer, ChatGPT-3.5, and five human raters. The scores assigned by the five human raters revealed a statistically significant low to high positive correlation. A slight to fair but significant level of agreement was observed in the scores assigned by ChatGPT-3.5 and two of the human raters. The findings suggest that reliable results could be obtained when the scores of an application and multiple human raters are considered and that ChatGPT may potentially assist human raters in assessing L2 college writing.
A Simple yet Strong Approach for Installation Prediction in ShareChat Ads

In order to comprehensively utilize user feedback signals, multi-task recommendation systems have been widely deployed across popular applications. For advertising recommendations, user conversion behaviors (e.g. install) are more important than other signals as they directly contribute to generating profits. The Recsys Challenge 2023 presents a task where the objective is to enhance the accuracy of install predictions based on user click and install feedback records. In this paper, we propose a simple-yet-strong solution SSL to effectively utilize click signals and achieve better installation prediction. SSL comprises three pluggable modules and incorporates comprehensive feature engineering techniques. The final submission solution involves an ensemble of six different models, ultimately achieving a score of 6.159096 and securing the 2nd place on the academic leaderboard.
Large Scale CVR Prediction through Hierarchical History Modeling

In this technical report, we present our 3rd place solution for the ACM RecSys 2023 Challenge. The annual challenge, sponsored and organized by ShareChat, focuses on a conversion rate (CVR) prediction task in online advertising to improve deep funnel optimization and user privacy. The historical features are important in CVR prediction, which elucidate the past engagements between users and ads. However, the identity of user and ads is unavailable in this challenge, making it a key challenge to identify their patterns. Our proposed method leverages several cluster method to generate possible pattern candidates, and conducts the history modeling hierarchically based on generated patterns. Then these designed features along with some other extracted features are involved in a ranking model to generate the install prediction. In the final submission, an ensemble of multiple results is selected to capture diverse facets of multiple models. Our team hahaha achieves an outstanding score of 5.904369 in normalised entropy in the test dataset, ranked 3rd in the competition finally.
Graph Enhanced Feature Engineering for Privacy Preserving Recommendation Systems

This paper presents a solution for RecSys Challenge 2023 which leverages 1) a novel feature classification method to categorize anonymous features into distinct groups and apply enhanced feature engineering and 2) data modeling as bipartite and similarity graphs where supervised and unsupervised learning can be applied to reveal underlying information in the form of new features and improve prediction accuracy of an ensemble of classifiers. Our team’s name is LearningFE, we rank 2nd on the leader board (score=5.892977).
Integrating Explicit and Implicit Feature Interactions for Online Ad Installation Forecasting

We present our solution for the RecSys Challenge 2023 in this paper, which focuses on online advertising and deep funnel optimization, emphasizing user privacy. The dataset provided for the challenge includes user and ad features, as well as click and install information from the ShareChat apps. The objective is to predict the probabilities of ad installations in the test set. Our solution primarily leverages the xDeepFM model, which combines explicit and implicit feature interactions to capture complex relationships. Additionally, we employ various techniques such as feature engineering, feature crossing, cross-validation, and model integration to enhance the performance of our solution. Through extensive experimentation and fine-tuning, our team BUAA_BIGSCity achieved a score of 6.282142 in the final submission, demonstrating the effectiveness of our approach. To promote reproducibility and further research, our code is available on GitHub 1. This paper provides insights into our solution for this challenge, showcasing advancements in online advertising and deep funnel optimization.
Technology and Deindustrialisation

Africa faces the multifaceted challenges of deindustrialisation and technological innovations. The challenges would not be threatening if leaders had developed good policies and strategies and implemented them effectively without having preying eyes. Contrarily, in the early days of independence of many African countries, African workers created developing, growing economies, and industrial towns were springing up in all corners of the continent; some of these towns were undergoing deindustrialisation. The destructive forces that retard the African development are the trajectories of poor technological development and deindustrialisation. These trajectories are the consequences of bad leadership in Africa. The book’s last chapter recommends that governments simultaneously adopt pragmatic economic strategies and interventionists to accelerate the outward orientation and revival of the African economy.
Classifying Token Frequencies Using Angular Minkowski p-Distance

Angular Minkowski p-distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski p-distances. Cosine dissimilarity is frequently used with datasets containing token frequencies, and angular Minkowski p-distance may potentially be an even better choice for certain tasks. In a case study based on the 20-newsgroups dataset, we evaluate classification performance for classical weighted nearest neighbours, as well as fuzzy rough nearest neighbours. In addition, we analyse the relationship between the hyperparameter p, the dimensionality m of the dataset, the number of neighbours k, the choice of weights and the choice of classifier. We conclude that it is possible to obtain substantially higher classification performance with angular Minkowski p-distance with suitable values for p than with classical cosine dissimilarity.
Digital Tools for Collecting and Presenting Information in Students and Teachers Training

The present work is devoted to the presentation of various digital tools used for collecting and presenting information. The main features of Padlet, Lino.it, Popplet, Book Creator are described and ready-made examples present the use of each of them. Toolkits are suitable for implementation in lessons in various subjects and teachers can work quickly and easy with them. They are used by teachers who have passed a qualification course conducted by the authors of the publication at the Department for Qualification and Professional Development of Pedagogical Specialists at University of Plovdiv Paisii Hilendarski, Plovdiv. They have been tested with students from fifth to seventh grade in Hristo Smirnenski Primary School, Rakovski.