Reviewerly/exHarmony huggingface dataset:
ChemoGraph: Interactive Visual Exploration of the Chemical Space

Abstract Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer

Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by updating only a few parameters so as to improve storage efficiency, called parameter-efficient transfer learning (PETL). Current PETL methods have shown that by tuning only 0.5% of the parameters, ViT can be adapted to downstream tasks with even better performance than full fine-tuning. In this paper, we aim to further promote the efficiency of PETL to meet the extreme storage constraint in real-world applications. To this end, we propose a tensorization-decomposition framework to store the weight increments, in which the weights of each ViT are tensorized into a single 3D tensor, and their increments are then decomposed into lightweight factors. In the fine-tuning process, only the factors need to be updated and stored, termed Factor-Tuning (FacT). On VTAB-1K benchmark, our method performs on par with NOAH, the state-of-the-art PETL method, while being 5x more parameter-efficient. We also present a tiny version that only uses 8K (0.01% of ViT's parameters) trainable parameters but outperforms full fine-tuning and many other PETL methods such as VPT and BitFit. In few-shot settings, FacT also beats all PETL baselines using the fewest parameters, demonstrating its strong capability in the low-data regime.
Compact Analog Chaotic Map Designs Using SOI Four-Gate Transistors

This work introduces three novel chaotic map circuits.Two of the map circuit use two pchannel and one n-channel silicon-on-insulator (SOI) four-gate transistor (G 4 FET) while the third design uses two n-channel and one p-channel G 4 FET.The multi-gate structure of G 4 FET is leveraged to obtain four independent bifurcation parameters in the chaotic map with a simple three-transistor design.A chaotic oscillator design is proposed using this discrete-time chaotic map circuit and the chaotic behavior is evaluated using bifurcation plot, Lyapunov exponent (LE), correlation coefficient, Shannon entropy, and Stability analysis.The application of this multi-parameter chaotic oscillator is presented in a chaos-based reconfigurable logic gate and the significant expansion of parameter design space compared to existing single-gate transistor-based maps is also demonstrated.Finally, a simple extension scheme for developing multi-dimensional robust chaotic map with even larger parameter space is presented and verified with specific instances of 2-D and 3-D maps.
Autonomous aerial obstacle avoidance using LiDAR sensor fusion

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.
AMSeg: A Novel Adversarial Architecture Based Multi-Scale Fusion Framework for Thyroid Nodule Segmentation

The thyroid gland is an important and essential endocrine organ for the human body that regulates metabolism, growth and development by secreting thyroid hormones.Thyroid nodules are irregular masses caused by lesions that can reflect the main clinical manifestation of thyroid abnormalities.Delineating the boundaries of thyroid nodules from ultrasound images is an indispensable part of computeraided diagnosis systems and medical imaging diagnosis for thyroid diseases.However, automatic segmentation of thyroid nodules is still a challenging task because of the similarity between the heterogeneous appearance and its background.In this study, we propose a novel framework for thyroid nodule segmentation that exploits multiscale anatomical features to build a late-stage fusion method based on adversarial training.The introduced architecture can handle blurred and uneven tissue regions during the thyroid nodule segmentation process.By adversarial training approaches, our segmentation block S adopts a framework with three different fusion scales, and discrimination block D employs a fully convolutional encoderdecoder architecture.Extensive experimental results demonstrate that AMSeg outperforms popular methods in terms of thyroid nodule segmentation.The proposed framework achieves Dice, Hd95 (95% Hausdorff distance), Jaccard, and precision values of 83.06%, 23.13, 74.18%, and 87.82%, respectively.As an end-toend network, the AMSeg can effectively replace manual segmentation methods and has great prospects in clinical applications.
Multi-modal adaptive gated mechanism for visual question answering

Visual Question Answering (VQA) is a multimodal task that uses natural language to ask and answer questions based on image content. For multimodal tasks, obtaining accurate modality feature information is crucial. The existing researches on the visual question answering model mainly start from the perspective of attention mechanism and multimodal fusion, which will tend to ignore the impact of modal interaction learning and the introduction of noise information in the process of modal fusion on the overall performance of the model. This paper proposes a novel and efficient multimodal adaptive gated mechanism model, MAGM. The model adds an adaptive gate mechanism to the intra- and inter-modality learning and the modal fusion process. This model can effectively filter irrelevant noise information, obtain fine-grained modal features, and improve the ability of the model to adaptively control the contribution of the two modal features to the predicted answer. In intra- and inter-modality learning modules, the self-attention gated and self-guided-attention gated units are designed to filter text and image features' noise information effectively. In modal fusion module, the adaptive gated modal feature fusion structure is designed to obtain fine-grained modal features and improve the accuracy of the model in answering questions. Quantitative and qualitative experiments on the two VQA task benchmark datasets, VQA 2.0 and GQA, proved that the method in this paper is superior to the existing methods. The MAGM model has an overall accuracy of 71.30% on the VQA 2.0 dataset and an overall accuracy of 57.57% on the GQA dataset.
Coordinating modalities of mathematical collaboration in shared VR environments

Abstract This study investigates how learners collaboratively construct embodied geometry knowledge in shared VR environments. Three groups of in-service teachers collaboratively explored six geometric conjectures with various virtual objects (geometric shapes) under the guidance of a facilitator. Although all the teachers were in different physical locations, they logged into a single virtual classroom with their respective groups and were able to see and manipulate the same geometric shapes as well as see their collaborators’ avatars and actions on the shapes in real time in the shared virtual space. This paper introduces a novel multimodal data analysis method for analyzing participants’ interactive patterns in collaborative forms of actions, gestures, movements, and speech. Results show that collaborative speech has a strong simultaneous relationship with actions on virtual objects and virtual hand gestures. They also showed that body movements and positions, which often focus on virtual objects and shifts in these movements away from or around the object, often signal key interactional collaborative events. In addition, this paper presents five emergent multimodality interaction themes showing participants’ collaborative patterns in different problem-solving stages and their different strategies in collaborative problem-solving. The results show that virtual objects can be effective media for collaborative knowledge building in shared VR environments, and that structured activity design and moderate realism may benefit shared VR learning environments in terms of equity, adaptability, and cost-effectiveness. We show how multimodal data analysis can be multi-dimensional, visualized, and conducted at both micro and macro levels.
Mobile learning behaviour of university students in Ghana

This study was planned to investigate the effect of distance education students’ attitudinal, social, and control beliefs on their mobile learning usage at the University of Ghana and the University of Education, Winneba. The Decomposed Theory of Planned Behaviour (DTPB) was modified to explain how students’ attitude, subjective norms, and behavioural control of mobile learning influenced their current mobile learning usage. The study used an explanatory sequential mixed-method approach. Congruent to that, structured questionnaires were administered to 400 distance learners selected by multi-stage sampling technique, and 20 distance learners selected by random sampling technique were interviewed via phone to collect data. Using correlation and multiple linear regression analysis, as well as hypothesis testing, the findings showed that, the university students’ attitudes towards mobile learning, subjective norms, and behavioral control insignificantly influenced their ongoing mobile usage thus providing meek support for the research model. However, it was found that university students’ mobile learning innovativeness, peer influence, and self-efficacy significantly affect their attitudes, subjective norms, and behavioral control respectively of their mobile learning usage. This supports the research model and can be inferred that university students’ mobile learning innovativeness, peer influence, and self-efficacy indirectly influence their mobile learning usage. The results of this study will enable educational institutions to engage in better strategic planning and implementation of mobile learning on a wider scale focusing on students’ behavioral, social, and control factors.
JKRL: Joint Knowledge Representation Learning of Text Description and Knowledge Graph

The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However, most methods only consider the triple structure in the knowledge graph and ignore the additional information related to the triple, especially the text description information. In this paper, we propose a knowledge graph representation model with a symmetric architecture called Joint Knowledge Representation Learning of Text Description and Knowledge Graph (JKRL), which models the entity description and relationship description of the triple structure for joint representation learning of knowledge and balances the contribution of the triple structure and text description in the process of vector learning. First, we adopt the TransE model to learn the structural vector representations of entities and relations, and then use a CNN model to encode the entity description to obtain the text representation of the entity. To semantically encode the relation descriptions, we designed an Attention-Bi-LSTM text encoder, which introduces an attention mechanism into the Bi-LSTM model to calculate the semantic relevance between each word in the sentence and different relations. In addition, we also introduce position features into word features in order to better encode word order information. Finally, we define a joint evaluation function to learn the joint representation of structural and textual representations. The experiments show that compared with the baseline methods, our model achieves the best performance on both Mean Rank and Hits@10 metrics. The accuracy of the triple classification task on the FB15K dataset reached 93.2%.
Sentiment analysis of amazon user reviews using a hybrid approach

Consumer reviews of services and products are a critical performance indicator for businesses looking to improve their contributions. They're also important for future customers to understand previous customers' experiences. Sentimental Analysis (SA) identifies patterns of sentiment in reviews by analyzing and extracting opinions from websites, papers, blogs, and other sources. The customer finds it difficult to locate a review for a specific aspect of a product they intend to purchase. Since it is difficult to go through a huge number of reviews, feature selection is used to reduce the data's dimensionality. Because choosing an appropriate feature can identify the product attributes that are disliked or discussed by consumers, selecting a feature is a significant step in sentiment analysis. In this paper, a Pearson correlation coefficient-based Harris Hawks Optimization – based Recurrent Neural Network-Long Short-Term Memory (PCCHHO-RNN-LSTM) algorithm is proposed to select features from reviews given by users for classifying their sentiments according to their appropriate polarity. In this proposed PCCHHO-RNNLSTM, to achieve greater accuracy, the correlation coefficients of features are used to conduct an initial dimensionality reduction. The HHO algorithm is then used to choose a small group of non-redundant features and RNN-LSTM is used to classify sentiments to their suitable polarity. The proposed method has been evaluated using the data gathered from Amazon. com's online product reviews. MATLAB software is used to implement the proposed work. From experimental results, the proposed PCCHH-RNNLSTM demonstrates considerable improvements of 95.8% accuracy, 95.4% precision, 95.6% recall, and 95.2% F-measure, respectively.
Examining Customer Experience in Using a Chatbot

While the use of artificial intelligence and chatbots is increasing, studies of customer experiences in using chatbots remain rare. This article explores the experience of customers before, during, and after their interaction with a chatbot to determine what aspects draw their attention when interacting with the chatbot. It identifies potential problems customers face when using a chatbot from different points of view, specifically those of a chatbot developer, a chatbot buyer, and a customer. This study focuses on VX, a chatbot from a leading telecommunications company in Indonesia, and uses interviews of fourteen customers, identified personas, and individual customer journeys to analyze interactions. The personas revealed five types of needs: two basic ones for information and transactions, and three others for security, entertainment, and complaints.
Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.
Global attention‐enabled texture enhancement network for MR image reconstruction

Purpose Although recent convolutional neural network (CNN) methodologies have shown promising results in fast MR imaging, there is still a desire to explore how they can be used to learn the frequency characteristics of multicontrast images and reconstruct texture details. Methods A global attention‐enabled texture enhancement network (GATE‐Net) with a frequency‐dependent feature extraction module (FDFEM) and convolution‐based global attention module (GAM) is proposed to address the highly under‐sampling MR image reconstruction problem. First, FDFEM enables GATE‐Net to effectively extract high‐frequency features from shareable information of multicontrast images to improve the texture details of reconstructed images. Second, GAM with less computation complexity has the receptive field of the entire image, which can fully explore useful shareable information of multi‐contrast images and suppress less beneficial shareable information. Results The ablation studies are conducted to evaluate the effectiveness of the proposed FDFEM and GAM. Experimental results under various acceleration rates and datasets consistently demonstrate the superiority of GATE‐Net, in terms of peak signal‐to‐noise ratio, structural similarity and normalized mean square error. Conclusion A global attention‐enabled texture enhancement network is proposed. it can be applied to multicontrast MR image reconstruction tasks with different acceleration rates and datasets and achieves superior performance in comparison with state‐of‐the‐art methods.
Adversarial ink: componentwise backward error attacks on deep learning

Abstract Deep neural networks are capable of state-of-the-art performance in many classification tasks. However, they are known to be vulnerable to adversarial attacks—small perturbations to the input that lead to a change in classification. We address this issue from the perspective of backward error and condition number, concepts that have proved useful in numerical analysis. To do this, we build on the work of Beuzeville, T., Boudier, P., Buttari, A., Gratton, S., Mary, T. and Pralet S. (2021) Adversarial attacks via backward error analysis. hal-03296180, version 3. In particular, we develop a new class of attack algorithms that use componentwise relative perturbations. Such attacks are highly relevant in the case of handwritten documents or printed texts where, for example, the classification of signatures, postcodes, dates or numerical quantities may be altered by changing only the ink consistency and not the background. This makes the perturbed images look natural to the naked eye. Such ‘adversarial ink’ attacks therefore reveal a weakness that can have a serious impact on safety and security. We illustrate the new attacks on real data and contrast them with existing algorithms. We also study the use of a componentwise condition number to quantify vulnerability.
Complexities of Managing a Mobile Phone Ban in the Digitalized Schools’ Classroom

This longitudinal qualitative study followed a teacher team at an upper secondary school during the implementation of a mobile phone ban during class, which was an initiative the team had jointly decided upon. Data consist of audio-recorded weekly team meetings, during which the teachers discussed their initiative. The teachers' implementation strategy was to inform the students about the ban at the start of the semester, and to collect their mobile phones before starting each class with the motivation that the ban would improve the learning environment. This strategy failed. Exceptions were made to the ban for several reasons. The collection procedure was cumbersome, time-consuming, and caused negative tensions between teachers and students. The team discussions made it apparent that with the teachers' consent, the students' mobile phones were already used as pedagogical tools complementary to other digital technologies in class.
Conditional pseudo-supervised contrast for data-Free knowledge distillation

Data-free knowledge distillation (DFKD) is an effective manner to solve model compression and transmission restrictions while retaining privacy protection, which has attracted extensive attention in recent years. Currently, the majority of existing methods utilize a generator to synthesize images to support the distillation. Although the current methods have achieved great success, there are still many issues to be explored. Firstly, the outstanding performance of supervised learning in deep learning drives us to explore a pseudo-supervised paradigm on DFKD. Secondly, current synthesized methods cannot distinguish the distributions of different categories of samples, thus producing ambiguous samples that may lead to an incorrect evaluation by the teacher. Besides, current methods cannot optimize the category-wise diversity samples, which will hinder the student model learning from diverse samples and further achieving better performance. In this paper, to address the above limitations, we propose a novel learning paradigm, i.e., conditional pseudo-supervised contrast for data-free knowledge distillation (CPSC-DFKD). The primary innovations of CPSC-DFKD are: (1) introducing a conditional generative adversarial network to synthesize category-specific diverse images for pseudo-supervised learning, (2) improving the modules of the generator to distinguish the distributions of different categories, and (3) proposing pseudo-supervised contrastive learning based on teacher and student views to enhance diversity. Comprehensive experiments on three commonly-used datasets validate the performance lift of both the student and generator brought by CPSC-DFKD. The code is available at https://github.com/RoryShao/CPSC-DFKD.git
HBert

With the emergence of a large-scale pre-training model based on the transformer model, the effect of all-natural language processing tasks has been pushed to a new level. However, due to the high complexity of the transformer's self-attention mechanism, these models have poor processing ability for long text. Aiming at solving this problem, a long text processing method named HBert based on Bert and hierarchical attention neural network is proposed. Firstly, the long text is divided into multiple sentences whose vectors are obtained through the word encoder composed of Bert and the word attention layer. And the article vector is obtained through the sentence encoder that is composed of transformer and sentence attention. Then the article vector is used to complete the subsequent tasks. The experimental results show that the proposed HBert method achieves good results in text classification and QA tasks. The F1 value is 95.7% in longer text classification tasks and 75.2% in QA tasks, which are better than the state-of-the-art model longformer.