Reviewerly/exHarmony huggingface dataset:
Cross-Attention Vision Transformer for Few-Shot Semantic Segmentation

Computer vision has experienced remarkable advancements thanks to the rapid progress of deep learning techniques and the availability of extensive reference datasets. However, training models in the presence of limited annotated data remains a challenging task due to data dependency. To address this issue, few-shot learning has emerged as a promising solution, particularly in domains such as medical image analysis, in which accurate segmentation is crucial despite limited sample availability. Typically, a few-shot semantic segmentation model consists of a CNN encoder, a CNN decoder, and a simple classifier that must be learned in most of the existing approaches. However, an effective adaptation of a model considering all three components to the new class is very difficult since there are only a few support set images available. In this work, we focus on reducing meta-learning task complexity by concentrating only on the classifier, using a pre-trained encoder-decoder. Thus, we propose a new method, named Cross-Attention in Classifier Weight Transformer (Cross-CWT), that explores cross-attention to dynamically adjust the classifier’s weights to each query image. Experimental results show that the proposed approach can achieve competitive performance compared to other state-of-the-art methods with lesser training costs, making the few-shot semantic segmentation task much more tractable.
A Deep Memory-Aware Attentive Model for Knowledge Tracing

With the rapid development of Internet technology, online learning has become a major learning method for students. As a key component of online learning, the knowledge tracing mainly predicts students' performance in future questions by tracing the changes of students' knowledge mastery during the learning process. According to our research, most current deep knowledge tracing models take more into account factors such as students' response time, number of questions, and difficulty of questions, ignoring some aspects of psychological research on students' forgetting behaviors. Secondly, although the current knowledge tracing models based on attention mechanism are better than the traditional methods in performance prediction, they cannot capture the complex relationship between questions and responses over time because the attention layer is too shallow. In the paper, we propose a novel deep memory-aware attentive knowledge tracing model named DMAKT based on Transformer architecture. DMAKT has an encoder-decoder structure where the question embedding sequences enter the self-attention layer of the encoder, and the response embedding sequences as well as the question embedding sequences enter the self-attention layer of the decoder. DMAKT can capture the complex relationship between questions and responses through deep self-attention. Furthermore, DMAKT proposes a novel encoder-decoder monotonic attention mechanism for emphasizing performance information on recent related and other questions by considering the time interval factor of students' forgetting behavior. Finally, our extensive experiments on three real datasets show that DMAKT model improves the AUC value by an average of 4.9% compared with existing deep knowledge tracing models.
Enhanced CNN Security based on Adversarial FGSM Attack Learning: Medical Image Classification

Convolutional Neural Networks (CNNs) have grown in popularity for clinical image processing applications like as Covid and cancer detection. A new study, however, shows that hostile attacks with modest, unnoticeable disruptions can damage deep healthcare learning systems. This creates safety issues about using these technologies in healthcare situations. In this study, we will look at the approaches used to fight adversarial attacks on medical imaging. Next, we intend to investigate the resilience of pre-trained CNN architectures, as well as LeNet5 and MobileNetV1 models against Fast Gradient Sign Method (FGSM) attacks in a medical healthcare application-based chest X-ray dataset. We discover that pre-trained CNN models are much more sensitive to antagonistic assaults than other models, due to key feature discrepancies between them and regular models. Finally, we propose to improve the CNN’ models security by investigating adversarial training. According to the numerical results, models with lower computational complexity and restricted layers are much more safe against malicious attacks than bigger models which are commonly utilized in medical healthcare systems.
Leveraging Implicit Knowledge in Recommender Systems for Maternal Health

Previous research has indicated that pregnant women acquire information more effectively through watching videos rather than consulting with experts. Therefore, understanding what they search for on video content platforms has become progressively crucial in domains like pregnancy in maternal health. Learning their viewing habits can help providers and public health officials target messaging to ensure proper and fair use of this resource, while mitigating the impact of any harmful content. Although researchers have carried out several studies to answer this question, none so far has utilized data mining, specifically recommender systems themselves, which as we show in this paper can be a tremendous source of information. This paper presents a novel approach to learn the viewership habits of mothers-to-be by leveraging implicit knowledge embedded in recommendations with human annotators. We illustrate the value of this approach by presenting examples in pregnancy and maternal health and showing the value of the constructed search map based on key metrics and comparisons to alternative approaches. Interestingly while a lot of research in data mining and recommender systems focuses on algorithms *for* recommendations, this paper demonstrates that there may be great value in learning *from* recommendations to inform issues such as health and policy.
Progressive DCT Image Compression Based On Quad Tree Decomposition for WSN

Wireless Sensor Networks (WSNs) have nodes with limited energy, limited processing power, and limited memory. In addition, they have low bandwidth and noisy communication channels. Therefore, image compression is very important for WSNs to reduce the time or the required bandwidth for transmitting the images. Progressive image compression is useful in WSNs because even when there is a cutoff in the transmitted data due to ending the battery lifetime or channel noise, the decoder can decompress the partially received data to obtain an acceptable image. Unfortunately, the progressive Discrete Cosine Transform (DCT) standard is unsuitable for WSNs because it uses the arithmetic coder or the Huffman coding that is very complex to implement. In this paper, we replace these coders with the Quad-Tree Decomposition (QTD) which has very low complexity. Unfortunately, most of the algorithms that use the QTD require lists to record the state of the image pixels and the quad-trees. These lists require an additional memory of about (2-3) of the image size. Therefore, it is not suitable for WSNs. In this research, we present the No List-QTD (NL-QTD) image compression algorithm that solves this memory constraint. It employs marker bits of an average memory of about 1/10 the image size instead of the lists to mark these states. The low complexity of the proposed NL-QTD also preserves the energy of the sensor's battery which in turn increases its lifetime. The simulation results show that the proposed method also has a higher Peak Signal-to-Noise Ratio (PSNR), and a higher Structural Similarity Index Measurement (SSIM), as compared with other related algorithms.
Designing a Cloud Computing System to Improve Energy Consumption in University Buildings

Cloud computing has developed many services that a person might need in their life, including safety, convenience, economy, and other services. In this research, a system that depends on cloud computing was designed and implemented to provide a specific service within the university, which is the ability to control, protect, and monitor the electrical appliances used. The Arduino app is used, including 6 electrical devices, some of which are based on sensors (temperature sensor and other motion sensors). Through a specific interface, the user can control the power consumption by turning off the unused devices. When the amount of current drawn or the amount of energy consumed is known, this system is set to 12 A if the quantity of current surpasses a particular limit. Low-power devices will be turned off, and then the current will be checked to ensure that it is below the limit. If so, the devices will remain on, and the condition will continue. The most important properties widely available for electrical appliances is protection. In this paper, the protection is represented if the voltage drops below 200 V or more than 230 V, whereby all electrical appliances will be turned off.
Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent

Conversational agents leveraging AI, particularly deep learning, are emerging in both academic research and real-world applications. However, these applications still face challenges, including disrespecting knowledge and facts, not personalizing to user preferences, and enormous demand for computational resources during training and inference. Recent research efforts have been focused on addressing these challenges from various aspects, including supplementing various types of auxiliary information to the conversational agents. However, existing methods are still not able to effectively and efficiently exploit relevant information from these auxiliary supplements to further unleash the power of the conversational agents and the language models they use. In this paper, we present a novel method, PK-NCLI, that is able to accurately and efficiently identify relevant auxiliary information to improve the quality of conversational responses by learning the relevance among persona, chat history, and knowledge background through lowlevel normalized contextual latent interaction. Our experimental results indicate that PK-NCLI outperforms the state-of-theart method, PK-FoCus, by 47.80%/30.61%/24.14% in terms of perplexity, knowledge grounding, and training efficiency, respectively, and maintained the same level of persona grounding performance. We also provide a detailed analysis of how different factors, including language model choices and trade-offs on training weights, would affect the performance of PK-NCLI.
Experimental Validation of PV Power Prediction with ML Models for Improved Grid Integration

The need of accurate prediction models for forecasting Renewable Energies (REs) production are increasing, especially for Photovoltaic (PV) power. In this paper, we implemented and tested two models for predicting PV power, known for their good performance in wind power and load forecasting. Our results proved that these two models have much higher performance than the empirical formula and the typical Machine Learning (ML) models such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), eXtreme Gradient Boosting (XG-Boost) and Long Short-Term Memory (LSTM). The first model is based on a combination of a CNN model and a Light Gradient-Boosting Machine (LightGBM) model, which decreased the Mean Squared Error (MSE), compared to a CNN model, by 7%. In the second model, we used the Variational Mode Decomposition (VMD) technique to decompose the PV power into components before training, and used LSTM and XGBoost for predicting each of the components. The second model decreased further the MSE by 45% (80% overall decrease compared to the empirical formula). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores

Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a β-variational autoencoder (VAE) with the discriminative strengths of generative adversarial networks (GANs), we propose a novel model, β-VAEGAN. We investigate methods for composing anomaly scores based on the discriminative and reconstructive capabilities of our model. Existing work focuses on linear combinations of these components to determine if data is anomalous. We advance existing work by training a kernelized support vector machine (SVM) on the respective error components to also consider nonlinear relationships. This improves anomaly detection performance, while allowing faster optimization. Lastly, we use the deviations from the Gaussian prior of β-VAEGAN to form a novel anomaly score component. In comparison to state-of-the-art work, we improve the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score during anomaly detection from 0.85 to 0.92 on the widely used MITBIH Arrhythmia Database. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Design and Simulation of Optical Neural Network

As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption. Scientists have concentrated on light, attempting to create neural network models in the optics field since the optical neural networks have apparent benefits including low power consumption, low latency, high balanced processing, high bandwidth, and increased speed. The optical neural network can significantly improve the performance of the required tasks by taking advantage of the characteristics. In this paper, we propose to train an optical neural network through nonlinear units by a saturable absorption (SA) to perform image classification tasks. The Neural networks are trained and tested with the help of the MNSIT dataset and EMNIST dataset after the successful completion of the code we get a system that can classify and process the images with an accuracy of 99.58 % in the MNIST dataset which is also supported by the graph. The backward propagating occurs using the back-propagation algorithm.
Arbitrary-Oriented Arabic Scene Text Annotation Based DeepAL and Scale-Aware Data Augmentation

One of the most important and widely utilized components of communication is visual text, which is widely employed in our daily interactions. Therefore, finding and using this textual information is quite important. The detection of Arabic text in scene images is a difficult and complex field of research. Scene text detection is affected by a complex background, various font sizes, and random orientations. Modern deep learning techniques have produced impressive results for both accuracy and precision values when used to detect text in natural scene images, while the images generated with these techniques cannot reproduce the complexity and variability of natural images. Wherefore, as a fast and efficient solution, we propose a deep active learning (DeepAL) detector system based on Scale-Aware Data Augmentation and Transfer Learning to reduce significantly the number of training samples required and also to minimize the annotation work for Arbitrary-Oriented Arabic scene text, we have used the Tunisia Street View Dataset (TSVD) [1].
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.
Benchmarking Deep Clustering Algorithms With ClustPy

Deep clustering algorithms have gained popularity as they are able to cluster complex large-scale data, like images. Yet these powerful algorithms require many decisions w.r.t. architecture, learning rate and other hyperparameters, making it difficult to compare different methods. A comprehensive empirical evaluation of novel clustering methods, however, plays an important role in both scientific and practical applications, as it reveals their individual strengths and weaknesses. Therefore, we introduce ClustPy, a unified framework for benchmarking deep clustering algorithms, and perform a comparison of several fundamental deep clustering methods and some recently introduced ones. We compare these methods on multiple well known image data sets using different evaluation metrics, perform a sensitivity analysis w.r.t. important hyperparameters and perform ablation studies, e.g., for different autoencoder architectures and image augmentation. To our knowledge this is the first in depth benchmarking of deep clustering algorithms in a unified setting.
Temporality-guided Masked Image Consistency for Domain Adaptive Video Segmentation

Video semantic segmentation has witnessed substantial advancements, largely due to the vast volume of labeled training samples. Nevertheless, domain adaptive video segmentation that adapts from a labeled source domain to an unlabeled target domain remains insufficiently delved into. In this paper, we propose Temporality-guided Masked Image Consistency (TgMIC), a simple yet effective approach that leverages the concept of Masked Image Modeling (MIM) to learn semantic features in the target domain. Unlike random masking strategy applied in traditional MIM, TgMIC introduces a novel temporality-guided masking strategy that samples the mask according to the distribution of optical flow, which facilitate the learning of spatial context relations in video sequence. Specifically, TgMIC masks the patches in vision transformers where the variance of optical flow is large, as these patches are known to contain noisy estimates of optical flow. In order to learn semantic information for video segmentation, TgMIC reconstructs the predictions of original frames from the masked frames. Comprehensive tests and detailed analysis on various public datasets show that our mechanism stands out, outpacing contemporaneous techniques, while ensuring no additional time costs.
A Joint Permute-and-Flip and Its Enhancement for Large-Scale Genomic Statistical Analysis

Owing to an increase in the amount of biomedical and healthcare data, privacy concerns regarding the use of genomic data have become well-recognized. Specifically, it is essential to develop personalized medicine to extract significant loci associated with diseases through large-scale genomic statistical analyses while protecting privacy. Although there are several differentially private methods for this purpose, they are too computationally complex to achieve high accuracy, and there is room for improvement in terms of the output error. In this study, we propose a novel mechanism, Joint Permute-and-Flip, that can provide higher-quality outputs than state-of-the-art techniques for top-K selection. We also present an efficient algorithm that can perform Joint Permute-and-Flip in $\mathcal{O}(m\log m)$ time when the dataset contains m elements, making it applicable even to large-scale analyses involving 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> elements. Additionally, we propose new score functions suitable for genomic statistical analysis that can be expressed as a single equation and achieve high accuracy. This is expected to facilitate the construction of accurate and efficient scores for a wider variety of genome statistics. Experimental results demonstrate that our Joint Permute-and-Flip method outperforms existing methods in terms of both accuracy and rank error and requires only half the run time of the exponential mechanism. The supplemental materials and the Python implementation of our experiments are available at https://github.com/ay0408/Joint-PnF.
Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets

Sentiment analysis involves using WordNets enriched with emotional metadata, which are valuable resources. However, manual annotation is time-consuming and expensive, resulting in only a few WordNet Lexical Units being annotated. This paper introduces two new techniques for automatically propagating sentiment annotations from a partially annotated WordNet to its entirety and to a WordNet in a different language: Multilingual Structured Synset Embeddings (MSSE) and Cross-Lingual Deep Neural Sentiment Propagation (CLDNS). We evaluated the proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish WordNet, which have many inter-lingual relations. Our results show that the MSSE+CLDNS method outperforms existing propagation methods, indicating its effectiveness in enriching WordNets with emotional metadata across multiple languages. This work provides a solid foundation for large-scale, multilingual sentiment analysis and is valuable for academic research and practical applications.
Intelligent Cognitive Clinician

In healthcare delivery, patients are diagnosed, medical tests are taken to be analysed to provide better treatment and test results are based on providing treatment to patients. Drug recommendation systems are widely used by medical professionals as medico-assisted systems, but recommendations are mute to surgeries or therapies. This paper presents a novel approach to designing an intelligent cognitive clinician (IC2) that diagnoses the patient using NLP algorithms for a speech of the patient to text translation or textual data for symptoms extraction and predict the disease and possible medical tests to be taken. Once the test results are available, the system recommends the drugs, therapies, or surgeries and patient feedback improves the treatment recommendation. So, IC2 is a drug, surgery, or therapy recommendation system based on symptoms, test results, and patient feedback.