Reviewerly/exHarmony huggingface dataset:
Joint design and compression of convolutional neural networks as a Bi-level optimization problem

Over the last decade, deep neural networks have shown great success in the fields of machine learning and computer vision. Currently, the CNN (convolutional neural network) is one of the most successful networks, having been applied in a wide variety of application domains, including pattern recognition, medical diagnosis and signal processing. Despite CNNs' impressive performance, their architectural design remains a significant challenge for researchers and practitioners. The problem of selecting hyperparameters is extremely important for these networks. The reason for this is that the search space grows exponentially in size as the number of layers increases. In fact, all existing classical and evolutionary pruning methods take as input an already pre-trained or designed architecture. None of them take pruning into account during the design process. However, to evaluate the quality and possible compactness of any generated architecture, filter pruning should be applied before the communication with the data set to compute the classification error. For instance, a medium-quality architecture in terms of classification could become a very light and accurate architecture after pruning, and vice versa. Many cases are possible, and the number of possibilities is huge. This motivated us to frame the whole process as a bi-level optimization problem where: (1) architecture generation is done at the upper level (with minimum NB and NNB) while (2) its filter pruning optimization is done at the lower level. Motivated by evolutionary algorithms' (EAs) success in bi-level optimization, we use the newly suggested co-evolutionary migration-based algorithm (CEMBA) as a search engine in this research to address our bi-level architectural optimization problem. The performance of our suggested technique, called Bi-CNN-D-C (Bi-level convolution neural network design and compression), is evaluated using the widely used benchmark data sets for image classification, called CIFAR-10, CIFAR-100 and ImageNet. Our proposed approach is validated by means of a set of comparative experiments with respect to relevant state-of-the-art architectures.
SafeGuard: Reducing the Security Risk from Row-Hammer via Low-Cost Integrity Protection

Row-Hammer (RH) is a DRAM data-disturbance failure that occurs when a row is activated frequently, which causes bit-flips in nearby rows. Row-Hammer is a significant security threat as an attacker can exploit the bit-flips to do privilege escalation and leak confidential data. While several solutions aim to mitigate RH, such solutions depend on the RH threshold and adversarial access patterns. Solutions developed for a given threshold become ineffective for newer devices with lower thresholds, and new attack patterns continue to break existing mitigations. Currently, there is no guaranteed solution for RH, which means that the system remains vulnerable to security threats even in the presence of RH mitigation.In this paper, we contend that simply relying on RH mitigation is insufficient to provide security in the presence of reducing threshold and motivated attackers. We propose SafeGuard, which equips the system with low-cost integrity protection as a defense against potential attacks that break the RH mitigation. As SafeGuard can detect arbitrary failures, it converts the problem of RH bit-flips from a security threat (silent consumption of corrupted data) to a reliability concern (detectable uncorrectable errors caused by integrity violation). We develop SafeGuard for systems that employ ECC modules and show that SafeGuard can provide strong detection to both SECDED (46-bit MAC per cache-line) and Chipkill (32-bit MAC per cache-line) while retaining the correction capability of conventional designs. SafeGuard avoids incurring any storage overheads in DRAM by simply reorganizing the ECC code to operate at a cache-line granularity (64 bytes) instead of a word granularity (8 bytes). Our evaluations show that SafeGuard has a negligible impact on both the system performance (0.7%) and the system reliability due to naturally occurring errors while still providing a strong defense against the security risk of RH by detecting arbitrary bit-flips.
Systematic Comparison of Vectorization Methods in Classification Context

Natural language processing has been the subject of numerous studies in the last decade. These have focused on the various stages of text processing, from text preparation to vectorization to final text comprehension. The goal of vector space modeling is to project words in a language corpus into a vector space in such a way that words that are similar in meaning are close to each other. Currently, there are two commonly used approaches to the topic of vectorization. The first focuses on creating word vectors taking into account the entire linguistic context, while the second focuses on creating document vectors in the context of the linguistic corpus of the analyzed texts. The paper presents the comparison of different existing text vectorization methods in natural language processing, especially in Text Mining. The comparison of text vectorization methods is possible by checking the accuracy of classification; we used the methods NBC and k-NN, as they are some of the simplest methods. They were used for the classification in order to avoid the influence of the choice of the method itself on the final result. The conducted experiments provide a basis for further research for better automatic text analysis.
Improving the cloud initialization in WRF-Solar with enhanced short-range forecasting functionality: The MAD-WRF model

In spite of WRF-Solar being a numerical weather prediction (NWP) model specifically tailored for solar energy applications, the model lacks a specific cloud initialization component to improve short-range predictions. To overcome this limitation we have combined the fundamental concepts of the Multi-sensor Advection Diffusion nowCast (MADCast) model of using satellite observations to detect the clouds and advect and diffuse the clouds within a NWP model, with the better cloud–aerosol–radiation physics of WRF-Solar, into a new model referred to as MAD-WRF. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis (liquid, ice, and snow hydrometeor content). During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode. In the nudging phase, there is a relaxation of the resolved hydrometeors towards the passive hydrometeors. Both modes aim to avoid an abrupt dissipation of clouds if the atmospheric environment is not adequate to support them. Both MAD-WRF active and passive modes as well as WRF-Solar have been run in a demonstration of MAD-WRF over the contiguous U.S., and results from these short-range forecasts (0–6 h) are presented here to illustrate the added value of MAD-WRF for global horizontal irradiance predictions. These results clearly illustrate the added value that MAD-WRF brings for short term irradiance predictions with the WRF-Solar model.
The 3E Optimal Location Assessment of Flat-Plate Solar Collectors for Domestic Applications in Iran

The analytical hierarchy process (AHP) was utilized to determine the optimal location on which to install flat-plate solar thermal collectors for residential buildings in a number of cities in Iran under diverse climatic conditions. The payback period of investment (IPBP) was chosen as one of the decision criteria, while payback periods of energy and greenhouse gas emissions (EPBP and GGEPBP), being two recently introduced concepts, were also taken into account to provide a broader insight from the energy, economic, and environmental (3E) benefits of the system. The novelty of this work is proposing a method to find places with the greatest potential to install flat-plate solar collectors. It was performed using AHP as a systematic decision-making tool, and based on energy, environmental, and economic criteria, which are the key aspects of an energy system. Codes developed in the MATLAB software were employed to determine the values for different investigated cities. According to the results, Yazd, located in the center of the country, was found to be the best place to install the system. This city enjoys EPBP, IPBP, and GGEPBP scores of 2.47, 3.37, and 0.71 years, respectively. The collector area for this city was also found to be 109.8 m2. Yazd gained a score of 26.5 out of 100. With scores of 24.4, 18.6, 15.9, and 14.6 out of 100, Tehran, Bandar Abbas, Rasht, and Tabriz were found to be the second, third, fourth, and fifth priorities for utilizing the system, respectively.
ArSL21L: Arabic Sign Language Letter Dataset Benchmarking and an Educational Avatar for Metaverse Applications

It is complicated for the PwHL (people with hearing loss) to make a relationship with social majority, which naturally demands an interactive auto computer systems that have ability to understand sign language. With a trending Metaverse applications using augmented reality (AR) and virtual reality (VR), it is easier and interesting to teach sign language remotely using an avatar that mimics the gesture of a person using AI (Artificial Intelligence)-based system. There are various proposed methods and datasets for English SL (sign language); however, it is limited for Arabic sign language. Therefore, we present our collected and annotated Arabic Sign Language Letters Dataset (ArSL21L) consisting of 14202 images of 32 letter signs with various backgrounds collected from 50 people. We benchmarked our ArSL21L dataset on state-of-the-art object detection models, i.e., 4 versions of YOLOv5. Among the models, YOLOv5l achieved the best result with COCOmAP of 0.83. Moreover, we provide comparison results of classification task between ArSL2018 dataset, the only Arabic sign language letter dataset for classification task, and our dataset by running classification task on in-house short video. The results revealed that the model trained on our dataset has a superior performance over the model trained on ArSL2018. Moreover, we have created our prototype avatar which can mimic the ArSL (Arabic Sign Language) gestures for Metaverse applications. Finally, we believe, ArSL21L and the ArSL avatar will offer an opportunity to enhance the research and educational applications for not only the PwHL, but also in general real and virtual world applications. Our ArSL21L benchmark dataset is publicly available for research use on the Mendeley.
A multi-stage multimodal framework for sentiment analysis of Assamese in low resource setting

Multimodality has shown to be helpful in several natural language processing tasks. Thus, adding multiple modalities to the traditional sentiment analysis has also proven to be useful. However, multimodality in a low resource setting for sentiment analysis is yet to be explored for several resource-constrained languages. Assamese is a low-resource language spoken mainly in the state of Assam in India. This paper presents an Assamese multimodal dataset comprising of 16,000 articles from the news domain as a benchmark resource. Secondly, we present a multi-stage multimodal sentiment analysis framework that concurrently exploits textual and visual cues to determine the sentiment. The proposed architecture encodes the news content collaboratively. The text branch encodes semantic content information by considering the semantic information of the news. At the same time, the visual branch encodes the visual appearance information from the news image. Then, an intermediate fusion-based multimodal framework is proposed to exploit the internal correlation between textual and visual features for joint sentiment classification. Finally, a decision-level fusion mechanism is employed on the three models to integrate cross-modal information effectively for final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance (89.3%) than the best unimodal features (85.6%). • A multimodal and multi-purpose Assamese news dataset is presented. • Two models are proposed for visual and textual modality for sentiment classification. • A multi-stage fusion approach is employed for multimodal sentiment classification. • Qualitative and quantitative analysis are carried out of the models.
Multi-population biogeography-based optimization algorithm and its application to image segmentation

In view of the shortcomings of Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization (WRBBO) in solving complex optimization problems, such as insufficient search ability and low search efficiency, an improved WRBBO, Multi-population BBO (MPBBO) is proposed. Firstly, a multi-population strategy is adopted: the whole sorted population is divided into 3 different subgroups (high-level, median-level and low-level) through golden section to get some population diversity. Secondly, an aptitude-based teaching approach is proposed to be beneficial to each subgroup’s development: a sinusoidal-scaled differential mutation operator is performed on the high-level subgroup to mostly get stronger exploration and reduce the computing cost, a heuristic crossover with dynamic fine adjustment is hybridized with a horizontal crossover and a vertical one to form a multi-crossover on the median-level subgroup to mainly get stronger exploitation, and a best agent guiding strategy is used on the low-level subgroup to improve the search ability. Finally, an information sharing way is adopted: all the individuals in 3 subgroups share information by merging and sorting them. The experimental results on the complex functions from CEC-2013 and CEC-2017 test sets show that MPBBO obtains stronger search ability and higher efficiency than WRBBO and quite a few other state-of-the-art algorithms. MPBBO is applied to image segmentation with fast and robust fuzzy c-means (FRFCM), and the results show that FRFCM-MPBBO has more significant advantages than its comparison methods. • An effective multi-population strategy is presented to get population diversity. • A sinusoidal-scaled differential mutation is adopted on the high-level subgroup. • A best agent guiding strategy is adopted on the low-level subgroup for exploration. • A multi-crossover is adopted on the median-level subgroup for exploitation. • Proposed algorithm has better performance on 86 functions and image segmentation.
RatUNet: residual U-Net based on attention mechanism for image denoising

Deep convolutional neural networks (CNNs) have been very successful in image denoising. However, with the growth of the depth of plain networks, CNNs may result in performance degradation. The lack of network depth leads to the limited ability of the network to extract image features and difficults to fuse the shallow image features into the deep image information. In this work, we propose an improved deep convolutional U-Net framework (RatUNet) for image denoising. RatUNet improves Unet as follows: (1) RatUNet uses the residual blocks of ResNet to deepen the network depth, so as to avoid the network performance saturation. (2) RatUNet improves the down-sampling method, which is conducive to extracting image features. (3) RatUNet improves the up-sampling method, which is used to restore image details. (4) RatUNet improves the skip-connection method of the U-Net network, which is used to fuse the shallow feature information into the deep image details, and it is more conducive to restore the clean image. (5) In order to better process the edge information of the image, RatUNet uses depthwise and polarized self-attention mechanism to guide a CNN for image denoising. Extensive experiments show that our RatUNet is more efficient and has better performance than existing state-of-the-art denoising methods, especially in SSIM metrics, the denoising effect of the RatUNet achieves very high performance. Visualization results show that the denoised image by RatUNet is smoother and sharper than other methods.
Reversible image data hiding based on scalable difference expansion

Reversible image data hiding technology can not only ensure that the receiver can extract the hidden data like the traditional data hiding technology, but also restore all the pixel values of the original image. Among the existing reversible image data hiding technologies, the method based on differential expansion is the most popular one. In the classical differential expansion method, a bit of data is hidden in the expanded difference of a pair of pixel values. However, expanding difference of different pixel pairs may have different effects on the fidelity of the host image, and some may cause pixel value overflow. In order to solve this problem, this paper proposes a scalable difference expansion based data hiding technology under the condition of meeting data hiding capacity. We use super pixel, more precisely pixel block, to replace a single pixel used in the traditional difference expansion as the basic carrier. Data is hidden in the expanded difference of a pair of super pixels. Adjusting the size of super pixels and/or the differential magnification can scale data hiding capacity and fidelity of stego image. Experimental results verify the feasibility of the method and our expected performance. Because the size and shape of super pixels can be adjusted, it can provide security for data hiding to some extent. In addition, the proposed algorithm does not involve complex operations such as frequency domain transform and inverse transform, so it can be applied to real-time reversible data hiding.
Optimal design of low- and high-rise building structures by Tribe-Harmony Search algorithm

In this paper, optimum design of building structures is conducted by metaheuristic algorithms due to the shortcomings of the conventional design methods in providing economical designs. The Harmony Search (HS) is utilized as the main algorithm which was developed based on the musical process of searching for the optimal condition of harmony to produce an appropriate search approach for design optimization purposes. Besides, the Tribe-Harmony Search (Tribe-HS) algorithm is also proposed for the first time in this paper to improve the performance of the HS algorithm which divides the HS's searching phase into three distinct phases called "tribes", lead the primary algorithm to prioritize global search in the early iterations while resolving local search in the later iterations. Three building structures with 135, 3860, and 8272 structural members are used as design examples to demonstrate the suggested method's capacity to solve challenging optimization problems. The recommended method's overall performance is compared to that of the conventional Harmony Search algorithm and ten alternative metaheuristic algorithms using a total of 30 independent runs in each instance for statistical reasons. The findings demonstrated that the suggested method outperformed the other metaheuristics for the study design instances.
Improving the search accuracy of differential evolution by using the number of consecutive unsuccessful updates

During the process of evolution, feedback information is frequently utilized to guide the following search and it has been proved to be very effective. However, the information of unsuccessful updates has not been fully utilized yet. Thus, it is important to exploit this information systematically, which will provide a reference for further researches. In this paper, we use the number of consecutive unsuccessful updates c i G to improve the search accuracy of differential evolution (DE) and propose a novel type of DE variant, called as consecutive unsuccessful updates-based DE (CUSDE). There are two operations in CUSDE. Firstly, a simple but efficient mutation strategy is developed, in which the base vector and terminal vector are selected based on the probability calculated from c i G . Secondly, a deletion mechanism is proposed to adaptively remove some inferior individuals with larger c i G values. Compared with other state-of-the-art DE variants on CEC 2005 and CEC 2017 benchmark sets, the experimental results confirm the superiority of CUSDE in improving the search accuracy.
Biomedical sensor image segmentation algorithm based on improved fully convolutional network

Effective use of biomedical sensor image can help locate diseased tissues and tissue structures clearly presented, and clinical diagnosis and treatment can assist doctors in making appropriate treatment plans. In order to efficiently process the images acquired by biomedical sensors, we propose a biomedical sensor image segmentation method with improved fully convolutional network, which firstly extracts the local spatial and frequency domain information of the images acquired by biomedical sensors and enhances the texture information of the images. Secondly, the background interference is suppressed by increasing the target region weights to refine the processing of the image and enhance the features of the image while reducing the information redundancy. It is experimentally proved that the model in this paper can effectively reduce the phenomenon of cell adhesion after image segmentation, has better segmentation effect and segmentation accuracy, and can more effectively utilize the images acquired by biomedical sensors.
Online multiple object tracking using joint detection and embedding network

• A novel architecture was proposed called YOLOTracker that performs online MOT by exploiting joint detection and embedding network, which shows the powerful and efficient performance in MOT benchmarks. • The Path Aggregation Network was harnessed to combine the lowresolution and the high-resolution information for intergrating texture features and semantic information to mitigate alignment of the Re-ID features. • The BNNeck is designed to avail the network for learning the best embeddings. The effectiveness of the two-stage progressive learning approach (BNNeck) is discussed and analyzed for ID-discriminative embedding of joinly trained detection and tracking. It effectively reduces the number of ID switches in MOT. The proposed tracker outperforms other state-of-the-art MOT trackers in terms of accuracy and efficiency on the three publicly challenging datasets of MOT15, MOT16 and MOT17. Multiple object tracking (MOT) generally employs the paradigm of tracking-by-detection, where object detection and object tracking are executed conventionally using separate systems. Current progress in MOT has focused on detecting and tracking objects by harnessing the representational power of deep learning. Since existing methods always combine two submodules in the same network, it is particularly important that they must be trained effectively together. Therefore, the development of a suitable network architecture for the end-to-end joint training of detection and tracking submodules remains a challenging issue. The present work addresses this issue by proposing a novel architecture denoted as YOLOTracker that performs online MOT by exploiting a joint detection and embedding network. First, an efficient and powerful joint detection and tracking model is constructed to accomplish instance-level embedded training, which can ensure that the proposed tracker achieves highly accurate MOT results with high efficiency. Then, the Path Aggregation Network is employed to combine low-resolution and high-resolution features for integrating textural features and semantic information and mitigating the misalignment of the re-identification features. Experiments are conducted on three challenging and publicly available benchmark datasets and results demonstrate the proposed tracker outperforms other state-of-the-art MOT trackers in terms of accuracy and efficiency.