Reviewerly/exHarmony huggingface dataset:
Distance-Aware eXplanation Based Learning

eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL augments loss functions to penalize a model based on deviation of its explanations from user annotation of image features. The literature on XBL mostly depends on the intersection of visual model explanations and image feature annotations. We present a method to add a distance-aware explanation loss to categorical losses that trains a learner to focus on important regions of a training dataset. Distance is an appropriate approach for calculating explanation loss since visual model explanations such as Gradient-weighted Class Activation Mapping (Grad-CAMs) are not strictly bounded as annotations and their intersections may not provide complete information on the deviation of a model’s focus from relevant image regions. In addition to assessing our model using existing metrics, we propose an interpretability metric for evaluating visual feature-attribution based model explanations that is more informative of the model’s performance than existing metrics. We demonstrate performance of our proposed method on three image classification tasks.
A Neural Computing-based Access Control Protocol for AI-driven Intelligent Flying Vehicles in Industry 5.0-assisted Consumer Electronics

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes.
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality.A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions.GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.
Open Source Dual-Purpose Acrobot and Pendubot Platform: Benchmarking Control Algorithms for Underactuated Robotics

Recent interest in the control of underactuated robots has surged significantly due to the impressive athletic behaviors shown by robots developed by, e.g., Boston Dynamics ( <ext-link ext-link-type="uri" xlink:href="https://www.bostondynamics.com" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.bostondynamics.com</ext-link> ), Agility Robotics ( <ext-link ext-link-type="uri" xlink:href="https://agilityrobotics.com/robots" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://agilityrobotics.com/robots</ext-link> ), and the Massachusetts Institute of Technology [1]. This gives rise to the need for canonical robotic hardware setups for studying underactuation and comparing learning and control algorithms for their performance and robustness. Similar to OpenAIGym [2] and Stable Baselines [3], which provide simulated benchmarking environments and baselines for reinforcement learning algorithms, there is a need for benchmarking learning and control methods on real canonical hardware setups. To encourage reproducibility in robotics and artificial intelligence research, these hardware setups should be affordable and easy to manufacture with off-the-shelf components, and the accompanying software should be open source. Acrobots and pendubots are classical textbook examples of canonical underactuated systems with strong nonlinear dynamics, and their swing-up and upright balancing is considered a challenging control problem, especially on real hardware.
When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain nonlocal feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-toline search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://github.com/TQTQliu/ET-MVSNet.
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation

One critical challenge in 6D object pose estimation from a single RGBD image is efficient integration of two different modalities, i.e., color and depth. In this work, we tackle this problem by a novel Deep Fusion Transformer (DFTr) block that can aggregate cross-modality features for improving pose estimation. Unlike existing fusion methods, the proposed DFTr can better model cross-modality semantic correlation by leveraging their semantic similarity, such that globally enhanced features from different modalities can be better integrated for improved information extraction. Moreover, to further improve robustness and efficiency, we introduce a novel weighted vector-wise voting algorithm that employs a non-iterative global optimization strategy for precise 3D keypoint localization while achieving near real-time inference. Extensive experiments show the effectiveness and strong generalization capability of our proposed 3D keypoint voting algorithm. Results on four widely used benchmarks also demonstrate that our method outperforms the state-of-the-art methods by large margins. Code is available at https://github.com/junzastar/DFTr_Voting.
The Stable Signature: Rooting Watermarks in Latent Diffusion Models

Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. We introduce an active content tracing method combining image watermarking and Latent Diffusion Models. The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature. A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that the Stable Signature is robust to image modifications. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep 10% of the content, with 90+% accuracy at a false positive rate below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> .
Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing

Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garments, we propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images by following multimodal prompts, such as text, human body poses, and garment sketches. We tackle this problem by proposing a new architecture based on latent diffusion models, an approach that has not been used before in the fashion domain. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner. Experimental results on these new datasets demonstrate the effectiveness of our proposal, both in terms of realism and coherence with the given multimodal inputs. Source code and collected multimodal annotations are publicly available at: https://github.com/aimagelab/multimodal-garment-designer.
E<sup>2</sup>VPT: An Effective and Efficient Approach for Visual Prompt Tuning

As the size of transformer-based, models continues to grow, fine-tuning these large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. Parameter-efficient learning has been developed to reduce the number of tunable parameters during fine-tuning. Although these methods show promising results, there is still a significant performance gap compared to full fine-tuning. To address this challenge, we propose an Effective and Efficient Visual Prompt Tuning (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> VPT) approach for large-scale transformer-based model adaptation. Specifically, we introduce a set of learnable key-value prompts and visual prompts into self-attention and input layers, respectively, to improve the effectiveness of model fine-tuning. Moreover, we design a prompt pruning procedure to systematically prune low importance prompts while preserving model performance, which largely enhances the model’s efficiency. Empirical results demonstrate that our approach outperforms several state-of-the-art baselines on two benchmarks, with considerably low parameter usage (e.g., 0.32% of model parameters on VTAB-1k). Our code is available at https://github.com/ChengHan111/E2VPT.
Low-Light Image Enhancement with Multi-stage Residue Quantization and Brightness-aware Attention

Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility of low-light images. Conventional LLIE methods often produce poor results because they neglect the effect of noise interference. Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods. However, most deep learning-based LLIE methods cannot yet fully exploit the guidance of auxiliary priors provided by normal-light images in the training dataset. In this paper, we propose a brightness-aware network with normal-light priors based on brightness-aware attention and residual-quantized codebook. To achieve a more natural and realistic enhancement, we design a query module to obtain more reliable normal-light features and fuse them with low-light features by a fusion branch. In addition, we propose a brightness-aware attention module to further improve the robustness of the network to the brightness. Extensive experimental results on both real-captured and synthetic data show that our method outperforms existing state-of-the-art methods.
Gaseous Vapor Emission (GVE) Technique for Detection of Petrol Adulteration at Point of Sale (POS) Terminals

A one-dimensional mathematical modelling and simulation of the riser reactor in a fluid catalytic cracking unit was presented in this work.  The seemingly increasing demand for lighter fractions of petroleum as source of energy has necessitated that the fluid catalytic cracking (FCC) unit be modelled with greater precision and accuracy. A more accurate model of the riser reactor that is one of the primary functional elements of the FCC unit is important in improving the performance of the entire unit. Salient issues in the presented model were the four-lump kinetic model for the cracking mechanism, slip factor consideration between the phases, exponential catalyst activity deactivation model and the instantaneous estimation of the temperature dependent specific heat capacities of reacting species during the reaction. The model exhibited the behaviour expected of a riser reactor based on comparison with some industrial plant data and previous simulations. Simulation results showed that increase in the catalyst to oil ratio (COR) led to increase in gasoline yield only to a particular limit beyond which the yield began to decline. Presented results showed reasonable set of parameters for operating the riser which could lead to better yield. Keywords: One-dimensional (1-D), FCC, riser, hydrodynamic, lump kinetic, modelling, simulation.
VSOUND REAL-TIME DIGITAL EMULATION OF THE ACOUSTIC VIOLIN

vSound is an integrated stand-alone system that processes the raw electrical output from an electric violin and produces an output signal which, when fed to an amplifier and loudspeaker, approximates closely the timbre of an acoustic instrument. The system comprises a selectable high-impedance preamplifier, a 24-bit sigma delta codec and a digital signal processor (DSP) operating at 590 million multiplications-accumulations per second (MMACs). The device holds in its memory up to fifty far-field impulse responses of wooden instruments, any one of which may be convolved in real-time with the input to synthesize the modified output. In addition, the unit incorporates a suite of other functions to enhance the tonal quality of the final output, including a high-resolution equalizer, an adjustable blender and bass cut filter. The blender, which combines the original with the processed signal, extends the scope of the system for use with acoustic instruments fitted with pickups on the bridge. The unit is controlled by a set of navigator-style buttons in conjunction with a simple display screen. Windows-based control software also allows the user to download new responses and to adjust the settings of the graphic or arbitrary equalizer. Initial tests using an impulse response obtained from a Guarneri del Ges� violin constructed in the early-mid 18th century suggest that the system significantly enhances the tonal quality and timbre of an electric instrument.
Fragments of Arabic Poetry on Papyrus

&#x0D; &#x0D; &#x0D; This article presents an edition of, and commentary on, a fragment of papyrus preserved in the collection of the J. Willard Marriott Library at the University of Utah (P.Utah 280). This well-preserved piece is a rare example of Arabic papyrus with lines of poetry. The text of the verses and their attribution in the papyrus will be studied in detail and traced through later sources, leading to a discussion on textual variation and authorship as well as on the value of papyrological evidence vis-à-vis the corpus of classical Arabic literature. It will be shown that lineages of text and attribution can be highly complex and therefore do not lend themselves to linear representation, e.g., in book form or simple HTML format. The article outlines the requirements for alternative means of representation for textual layers and points to existing tools in Arabic studies and other disciplines.&#x0D; &#x0D; &#x0D;
Development of Fractal Equations of National Design Patterns based on the Method of R-Function

The paper is dedicated to the development of fractal equations of national design patterns based on the method of R-function (RFM). Using the equation of the line, the equation of the circle, and the structural means of the method of R-functions R0: R-conjunctions, R-disjunctions and R-reflections constructed various tree-like fractals, equations of fractals, consisting of intersections, tangents of circles. By setting the number of iterations n and the angle of inclination  various pre-fractals are generated based on these equations. These fractals are very beautiful, which can be used in creating computer landscapes, in various illustrations, telecommunications, the textile industry, when drawing patterns in ceramic and porcelain products, as well as developing patterns of modern design of Uzbek national carpets and costumes, etc.
Telugu and Hindi Script Recognition using Deep learning Techniques

The need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.
NLP Based Text Analytics and Visualization of Political Speeches

This paper presents a case study of implementing computational methods like Natural Language Processing (NLP) to perform Text Analytics and Visualization on political speech transcripts. The speech transcripts are published on websites, social media, and documents in large volumes and multiple languages. These transcripts are available in unstructured textual format and thus they are a part of big-data requiring analytics to derive insights from it. In this experiment, a significantly large volume of speech transcripts are analyzed and graphical visualizations are generated such as Lexical Dispersion Plot, Time Series Plot, WordCloud, Bar-Graphs using various Python libraries. The study has been useful in identifying issues highlighted across a large number of speech transcripts. So far, the linguists have tried to perform analysis using manual linguistic approaches which are extremely time-consuming and complex to understand the Political Discourse. Our experiment of applying NLP based text analytics proves to be a very efficient technique for Political Discourse Analysis (PDA).
Multimodal Decision-level Group Sentiment Prediction of Students in Classrooms

Sentiment analysis can be used to study an individual or a group’s emotions and attitudes towards other people and entities like products, services, or social events. With the advancements in the field of deep learning, the enormity of available information on internet, chiefly on social media, combined with powerful computing machines, it’s just a matter of time before artificial intelligence (AI) systems make their presence in every aspect of human life, making our lives more introspective. In this paper, we propose to implement a multimodal sentiment prediction system that can analyze the emotions predicted from different modal sources such as video, audio and text and integrate them to recognize the group emotions of the students in a classroom. Our experimental setup involves a digital video camera with microphones to capture the live video and audio feeds of the students during a lecture. The students are advised to provide their digital feedback on the lecture as ‘tweets’ on their twitter account addressed to the lecturer’s official twitter account. The audio and video frames are separated from the live streaming video using tools such as lame and ffmpeg. A twitter API was used to access and extract messages from twitter platform. The audio and video features are extracted using Mel-Frequency Cepstral Co-efficients (MFCC) and Haar Cascades classifier respectively. The extracted features are then passed to the Convolutional Neural Network (CNN) model trained on the FER2013 facial images database to generate the feature vector for classification of video-based emotions. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), trained on speech emotion corpus database was used to train on the audio features. A lexicon-based approach with senti-word dictionary and learning based approach with custom dataset trained by Support Vector Machines (SVM) was used in the twitter-texts based approach. A decision-level fusion algorithm was applied on these three different modal schemes to integrate the classification results and deduce the overall group emotions of the students. The use-case of this proposed system will be in student emotion recognition, employee performance feedback, monitoring or surveillance-based systems. The implemented system framework was tested in a classroom environment during a live lecture and the predicted emotions demonstrated the classification accuracy of our approach.