Reviewerly/exHarmony huggingface dataset:
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERTde are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community.
Domain Hybrid Day-Ahead Solar Radiation Forecasting Scheme

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.
Chance-constrained path planning in narrow spaces for a dubins vehicle

The problem of optimal path planning through narrow spaces in an unstructured environment is considered. The optimal path planning problem for a Dubins agent is formulated as a chance-constrained optimal control problem (CCOCP), wherein the uncertainty in obstacle boundaries is modelled using standard probability distributions. The chance constraints are transformed to deterministic equivalents using the inverse cumulative distribution function and subsequently incorporated into a deterministic optimal control problem. Due to multiple convex sub-regions introduced by the obstacles, the initial guess provided to optimal control solver is crucial for computation time and optimality of the solution. A constrained Delaunay triangulation mesh based approach is developed that ensures the initial guess to lie in the optimal sub-convex region. Finally, off-the-shelf software is used to transcribe the optimal control problem to a nonlinear program (NLP) using Gaussian quadrature orthogonal collocation and solved to obtain an optimal path that conforms to system dynamics. By varying the upper bound on probability of obstacle collision, a family of solutions is generated, parameterized by the risk associated with each solution. This approach enables discovery of special “keyhole trajectories” that can provide significant cost savings in a tightly-spaced obstacle field. Merits of this approach are illustrated by comparing it with the traditional bounded uncertainty approach.
Day-Ahead Prediction of Distributed Regional-Scale Photovoltaic Power

Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier to predict the performance of a few large-scale photovoltaic (PV) systems, a large number of small-scale PV systems with a wide geographical spread poses more challenges because they are often not metered for real-time monitoring. This paper proposes an artificial neural network (ANN)-based model to achieve regional-scale day-ahead PV power forecasts based on weather variables from numerical weather predictions (excluding solar irradiance) as inputs. The model was first implemented by dividing a region into clusters and selecting a representative site for each cluster using data dimension reduction algorithms. Solar irradiance forecasts were then generated for each representative PV system and the corresponding PV power was simulated. The cluster power output was obtained using a linear upscaling model and summed to produce regional-scale power forecasts. The model’s accuracy is validated using power generation data of several distributed systems in California. The results show at least a 29-percent root mean square error reduction over the benchmarking models.
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval

Previous Knowledge Distillation based efficient image retrieval methods employs a lightweight network as the student model for fast inference. However, the lightweight student model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training. To dynamically adjust the model capacity, our dynamic framework inserts a learnable convolutional layer within each residual block in the student model as the channel importance indicator. The indicator is optimized simultaneously by the image retrieval loss and the compression loss, and a retrieval-guided gradient resetting mechanism is proposed to release the gradient conflict. Extensive experiments show that our method has superior inference speed and accuracy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, our method saves 67.13% model parameters and 65.67% FLOPs (around 24.13% and 21.94% higher than state-of-the-arts) without sacrificing accuracy (around 2.11% mAP higher than state-of-the-arts).
Closed-set automatic speaker identification using multi-scale recurrent networks in non-native children

Children may benefit from automatic speaker identification in a variety of applications, including child security, safety, and education. The key focus of this study is to develop a closed-set child speaker identification system for non-native speakers of English in both text-dependent and text-independent speech tasks in order to track how the speaker's fluency affects the system. The multi-scale wavelet scattering transform is used to compensate for concerns like the loss of high-frequency information caused by the most widely used mel frequency cepstral coefficients feature extractor. The proposed large-scale speaker identification system succeeds well by employing wavelet scattered Bi-LSTM. While this procedure is used to identify non-native children in multiple classes, average values of accuracy, precision, recall, and F-measure are being used to assess the performance of the model in text-independent and text-dependent tasks, which outperforms the existing models.
Efficient lattice-based revocable attribute-based encryption against decryption key exposure for cloud file sharing

Cloud file sharing (CFS) has become one of the important tools for enterprises to reduce technology operating costs and improve their competitiveness. Due to the untrustworthy cloud service provider, access control and security issues for sensitive data have been key problems to be addressed. Current solutions to these issues are largely related to the traditional public key cryptography, access control encryption or attribute-based encryption based on the bilinear mapping. The rapid technological advances in quantum algorithms and quantum computers make us consider the transition from the tradtional cryptographic primitives to the post-quantum counterparts. In response to these problems, we propose a lattice-based Ciphertext-Policy Attribute-Based Encryption(CP-ABE) scheme, which is designed based on the ring learing with error problem, so it is more efficient than that designed based on the learing with error problem. In our scheme, the indirect revocation and binary tree-based data structure are introduced to achieve efficient user revocation and dynamic management of user groups. At the same time, in order to further improve the efficiency of the scheme and realize file sharing across enterprises, the scheme also allows multiple authorities to jointly set up system parameters and manage distribute keys. Furthermore, by re-randomizing the user's private key and update key, we achieve decryption key exposure resistance(DKER) in the scheme. We provide a formal security model and a series of security experiments, which show that our scheme is secure under chosen-plaintext attacks. Experimental simulations and evaluation analyses demonstrate the high efficiency and practicality of our scheme.
Deep Learning Approach for Sign Language Recognition Using DenseNet201 with Transfer Learning

Transfer learning has been utilized to solve many complex real-world problems. Over the last several years, transfer learning had many applications in image and video recognition. To get higher recognition rates, deep and wider architectures of the convolutional neural networks (CNN) have been designed. In this research, we proposed a novel transfer learning-based model using a popular CNN architecture called DenseNet201 for the recognition of Indian Sign Language (ISL) hand gestures. We applied transfer learning to DenseNet201 by freezing some of its layers to retain its knowledge of generalization and fine-tuning the remaining layers for ISL dataset. Pre-trained DenseNet201 was used to extract the features of the gesture images. To classify the ISL gesture, custom layers were added to the pretrained DenseNet201 model. The proposed model helped to achieve higher accuracy of 100%.
Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities

Rooftop photovoltaics (PVs) are considered a promising solution to alleviating current cities’ escalating energy usage and carbon emissions. In high-density cities, complex shading effects and rooftop availabilities (caused by diversified rooftop obstacles and irregular rooftop outlines) jointly make planning of large-scale distributed rooftop PV systems critically challenging. This study proposed an optimal packing and planning method for large-scale distributed rooftop PV systems under complex shading and rooftop availabilities, tackling the challenges by decoupling optimal packing and planning into two-step optimization. Utilizing horizontal-level genetic algorithm, the method first optimized PV-panels packing on irregular-shaped rooftops to maximize area utilization. Second, adopting sequential integer linear programming, the method optimized the planning of individual rooftop packing levels to minimize levelized cost of electricity (LCOE). Based on a 139-rooftop Hong Kong case study, the method was verified against 1 billion Monte-Carlo solutions, which reduced LCOE by 48.0% at most and achieved the lowest LCOE of 0.365 HKD/kWh. Further analysis showed that the proposed method outperformed a rule-based planning method because of its better utilization of high solar-energy-intensity areas, reducing the LCOE by 15.4%. In practice, the method can be used to facilitate deployment of large-scale distributed rooftop PV, enhancing overall system cost-effectiveness and city decarbonization.
Pedestrian multiple-object tracking based on FairMOT and circle loss

Multi-object Tracking is an important issue that has been widely investigated in computer vision. However, in practical applications, moving targets are often occluded due to complex changes in the background, which leads to frequent pedestrian ID switches in multi-object tracking. To solve the problem, we present a multi-object tracking algorithm based on FairMOT and Circle Loss. In this paper, HRNet is adopted as the baseline. Then, Polarized Self-Attention is added to HRNet-w32 to obtain weights of helpful information based on its modeling advantages. Moreover, the re-identification branch is optimized, and the Circle Loss is selected as the loss function to acquire more discriminative pedestrian features and to distinguish different pedestrians. The method proposed is assessed on the public MOT17 datasets. The experimental results show that the MOTA score achieves 69.5%, IDF1 reaches 70.0%, and the number of ID switches (IDs) decreases 636 times compared to the TraDes algorithm.
Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques

Abstract Vision-based analysis of waterbodies can provide important information required for monitoring, analyzing, and managing water resource systems, such as visual flood detection, delineation, and mapping. Water, however, is an ornery object in image processing, as it can be found in different forms and colors in nature. This makes the detection, classification, and tracking of water in images and videos difficult for computer vision models. There are still visual differences resulting from water texture and its inherent optical properties associated with different waterbodies which can be recognized and extracted to support computer models to better analyze water images. This study aims to utilize a set of early, mid-level, and high-level vision techniques, including Gabor kernels, local binary patterns (LBPs), and deep learning (DL) models to extract and analyze water texture and color of different waterbodies in digital images. For this purpose, ATLANTIS TeXture (ATeX), an image dataset for waterbodies classification and texture analysis, was used. Models were trained for the task of classification on ATeX. Then, the performance of each model in extracting texture features was evaluated and compared. Results showed that the classification accuracy achieved by the Gabor magnitude tensor, LBP, and DL model (ShuffleNet V2 × 1.0) are 29, 35, and 92%, respectively, and thus the DL model outperforms traditional vision-based techniques. Moreover, the classification results on raw images represented by different color spaces (e.g., RGB, HSV, etc.) emphasized the importance of color information for digital image processing of water. Analyzing representative visual features and properties of different water types and waterbodies can facilitate designing a customized Convolutional Neural Networks (CNNs) for water scenes, as CNNs recognize objects through the analysis of both texture and shape clues and their relationship in the entire field of view.
Vision–Language Model for Visual Question Answering in Medical Imagery

In the clinical and healthcare domains, medical images play a critical role. A mature medical visual question answering system (VQA) can improve diagnosis by answering clinical questions presented with a medical image. Despite its enormous potential in the healthcare industry and services, this technology is still in its infancy and is far from practical use. This paper introduces an approach based on a transformer encoder-decoder architecture. Specifically, we extract image features using the vision transformer (ViT) model, and we embed the question using a textual encoder transformer. Then, we concatenate the resulting visual and textual representations and feed them into a multi-modal decoder for generating the answer in an autoregressive way. In the experiments, we validate the proposed model on two VQA datasets for radiology images termed VQA-RAD and PathVQA. The model shows promising results compared to existing solutions. It yields closed and open accuracies of 84.99% and 72.97%, respectively, for VQA-RAD, and 83.86% and 62.37%, respectively, for PathVQA. Other metrics such as the BLUE score showing the alignment between the predicted and true answer sentences are also reported.
A Fundamental Non-Classical Logic

We give a proof-theoretic as well as a semantic characterization of a logic in the signature with conjunction, disjunction, negation, and the universal and existential quantifiers that we suggest has a certain fundamental status. We present a Fitch-style natural deduction system for the logic that contains only the introduction and elimination rules for the logical constants. From this starting point, if one adds the rule that Fitch called Reiteration, one obtains a proof system for intuitionistic logic in the given signature; if instead of adding Reiteration, one adds the rule of Reductio ad Absurdum, one obtains a proof system for orthologic; by adding both Reiteration and Reductio, one obtains a proof system for classical logic. Arguably neither Reiteration nor Reductio is as intimately related to the meaning of the connectives as the introduction and elimination rules are, so the base logic we identify serves as a more fundamental starting point and common ground between proponents of intuitionistic logic, orthologic, and classical logic. The algebraic semantics for the logic we motivate proof-theoretically is based on bounded lattices equipped with what has been called a weak pseudocomplementation. We show that such lattice expansions are representable using a set together with a reflexive binary relation satisfying a simple first-order condition, which yields an elegant relational semantics for the logic. This builds on our previous study of representations of lattices with negations, which we extend and specialize for several types of negation in addition to weak pseudocomplementation. Finally, we discuss ways of extending these representations to lattices with a conditional or implication operation.
Methodology for Predictive Cyber Security Risk Assessment (PCSRA)

With the current impulse of Cyberattacks, data becomes of central importance. There are many challenges in how they are used that also need to be discussed. Defining the suitable cybersecurity incident response model is a critical challenge that all companies face today. With a high number of incidents that happen daily and for which there is not always an adequate response due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that can cause such incidents, in and consistently trying to have the most appropriate answer, and ultimately, boosting responsiveness and success. At the same time, there are several different methodologies to assess the risk management of organizations and their level of maturity. The capability assessment is intended to enable organizations to understand better the fundamentals that need to be laid down to deliver cybersecurity successfully. There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully embracing cybersecurity and an even more significant gap in assessing it from a risk management perspective. This work proposes a model to assess this capacity.
Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning methods. A total of 3,360 patches were used from the Digital Database for Screening Mammography (DDSM) and CBIS-DDSM mammogram databases for convolutional neural network training and testing. Transfer learning was applied using Resnet50, Xception, NASNet, and EfficientNet-B7 network backbones. The best classification performance was achieved by the Xception network. On the original CBIS-DDSM test data, an AUC of 0.9317 was obtained for the five-way classification problem. The results are promising for the implementation of automated diagnosis of breast cancer.
Dynamically Expandable Graph Convolution for Streaming Recommendation

Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models solely conduct a one-time training-test fashion and can hardly adapt to evolving demands, considering user preference shifts and ever-increasing users and items in the real world. To tackle such challenges, the streaming recommendation is proposed and has attracted great attention recently. Among these, continual graph learning is widely regarded as a promising approach for the streaming recommendation by academia and industry. However, existing methods either rely on the historical data replay which is often not practical under increasingly strict data regulations, or can seldom solve the \textit{over-stability} issue. To overcome these difficulties, we propose a novel \textbf{D}ynamically \textbf{E}xpandable \textbf{G}raph \textbf{C}onvolution (DEGC) algorithm from a \textit{model isolation} perspective for the streaming recommendation which is orthogonal to previous methods. Based on the motivation of disentangling outdated short-term preferences from useful long-term preferences, we design a sequence of operations including graph convolution pruning, refining, and expanding to only preserve beneficial long-term preference-related parameters and extract fresh short-term preferences. Moreover, we model the temporal user preference, which is utilized as user embedding initialization, for better capturing the individual-level preference shifts. Extensive experiments on the three most representative GCN-based recommendation models and four industrial datasets demonstrate the effectiveness and robustness of our method.