Reviewerly/exHarmony huggingface dataset:
An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)

Image captioning by the encoder-decoder framework has shown tremendous advancement in the last decade where CNN is mainly used as encoder and LSTM is used as a decoder. Despite such an impressive achievement in terms of accuracy in simple images, it lacks in terms of time complexity and space complexity efficiency. In addition to this, in case of complex images with a lot of information and objects, the performance of this CNN-LSTM pair downgraded exponentially due to the lack of semantic understanding of the scenes presented in the images. Thus, to take these issues into consideration, we present CNN-GRU encoder decode framework for caption-to-image reconstructor to handle the semantic context into consideration as well as the time complexity. By taking the hidden states of the decoder into consideration, the input image and its similar semantic representations is reconstructed and reconstruction scores from a semantic reconstructor are used in conjunction with likelihood during model training to assess the quality of the generated caption. As a result, the decoder receives improved semantic information, enhancing the caption production process. During model testing, combining the reconstruction score and the log-likelihood is also feasible to choose the most appropriate caption. The suggested model outperforms the state-of-the-art LSTM-A5 model for picture captioning in terms of time complexity and accuracy.
Formalization of Orthogonal Decomposition for Hilbert Spaces

Summary In this article, we formalize the theorems about orthogonal decomposition of Hilbert spaces, using the Mizar system [1], [2]. For any subspace S of a Hilbert space H , any vector can be represented by the sum of a vector in S and a vector orthogonal to S. The formalization of orthogonal complements of Hilbert spaces has been stored in the Mizar Mathematical Library [4]. We referred to [5] and [6] in the formalization.
Prime Representing Polynomial with 10 Unknowns

Summary In this article we formalize in Mizar [1], [2] the final step of our attempt to formally construct a prime representing polynomial with 10 variables proposed by Yuri Matiyasevich in [4]. The first part of the article includes many auxiliary lemmas related to multivariate polynomials. We start from the properties of monomials, among them their evaluation as well as the power function on polynomials to define the substitution for multivariate polynomials. For simplicity, we assume that a polynomial and substituted ones as i -th variable have the same number of variables. Then we study the number of variables that are used in given multivariate polynomials. By the used variable we mean a variable that is raised at least once to a non-zero power. We consider both adding unused variables and eliminating them. The second part of the paper deals with the construction of the polynomial proposed by Yuri Matiyasevich. First, we introduce a diophantine polynomial over 4 variables that has roots in integers if and only if indicated variable is the square of a natural number, and another two is the square of an odd natural number. We modify the polynomial by adding two variables in such a way that the root additionally requires the divisibility of these added variables. Then we modify again the polynomial by adding two variables to also guarantee the nonnegativity condition of one of these variables. Finally, we combine the prime diophantine representation proved in [7] with the obtained polynomial constructing a prime representing polynomial with 10 variables. This work has been partially presented in [8] with the obtained polynomial constructing a prime representing polynomial with 10 variables in Theorem (85).
Vehicle door frame positioning method for binocular vision robots based on improved YOLOv4

Abstract In the task of using robots to capture end-of-life cars, the position of the vehicle door frame needs to be grasped. A fast and accurate positioning of the vehicle door frame is the key to realizing the automatic car grasping process. Traditional methods for locating and grasping scrap cars rely heavily on manual operations and suffer from low grasping efficiency and poor accuracy. Therefore, this paper proposes a binocular vision robot vehicle door frame spatial localization method based on the improved YOLOv4. This method includes a lightweight and efficient feature fusion target detection network in complex environments, and the target detection results are combined with an enhanced SURF feature–matching method to locate the vehicle door frame position. To simplify the network structure, MobileNetv3 is used instead of the backbone network CSPDarknet53, and deep separable convolution is used in the network. To increase the sensitivity of the network to vehicle door frame targets in complex environments, an improved convolutional block attention module is added to the pyramid attention with simple network backbones. Moreover, adaptive spatial feature fusion is introduced into the network to fully use the features at different scales for more effective feature fusion. Compared with YOLOv4, the number of network parameters is reduced by 73.8%, the mAP is improved by 1.35%, and the detection speed is increased by 28.7%. The experimental results demonstrate that the positioning accuracy of the system is 0.745 mm, which meets the positioning measurement error of less than 1 cm required for the vehicle door frame. The paper also compares our findings with other network models. The results show that the method achieves a good balance between detection speed and detection accuracy, satisfying the task of identifying vehicle door frames in complex environments with good detection results.
BYEL: Bootstrap Your Emotion Latent

With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, which learns from synthetic images and infers from real images, is one of the most interesting areas. Generally, domain adaptation methods are widely used to address LSD challenges, but the limitation is that the target domains (real images) are still needed. Focusing on these limitations, we propose a framework Bootstrap Your Emotion Latent (BYEL), which uses only synthetic images in training. BYEL is implemented by adding Emotion Classifiers and Emotion Vector Subtraction to the BYOL framework that performs well in self-supervised representation learning. We trained our framework using synthetic images generated from the Aff-wild2 dataset and evaluated it using real images from the Aff-wild2 dataset. The result shows that our framework (0.3084) performs 2.8% higher than the baseline (0.3) on the macro F1 score metric.
Quantum identity authentication using a Hadamard gate based on a GHZ state

Abstract According to the properties of the Hadamard gate and GHZ state, a quantum authentication protocol with transferable information is proposed. This protocol has higher security and integrity. Specifically, the direct appearance of the key and the transmitted information are not required for both quantum and classical channels. In addition, the protocol realizes the key update in the process of identity authentication, which can automatically generate the key needed for the next information transmission. We expect that our protocol can be extended to many other quantum communication technologies.
Project-Based Learning in Higher Education as a Tool for Integrated Hard, Soft Skills and Engineering Language Development

Modern multilingual world provokes university graduates to master not only natural languages, but also the languages of signs and technologies. Multilingualism, as a new form of literacy, implies mastering other ways of communication, not only verbal-linguistic ones. The paper considers the impact of project-based learning on the formation of traditional hard skills, necessary soft skills, and mastering of visual-image engineering language, which implies publicly available presentation of the results of project activities. The experience of Ural Federal University in organizing project competitions on behalf of employers’ partners aimed at solving urgent problems of the society is considered. Close interaction between employers and the university allows employers to participate in training future employees for their company, solving their production problems, and universities to improve the quality of the educational process and the demand for graduates in the labor market. The use of the project-based learning method allows students on the one hand to consistently explore and delve into the details of the problem under study, using the hard skills available. Group work on projects develops soft skills that involve communication and collaborative project work in a project team. The need to present the results of one’s work enhances engineering language proficiency. The results of the survey of the participants of the project activities are presented. It is concluded that the educational tool used is successful. The obtained research results have practical significance and can be used in the organization of the educational process in higher educational institutions.
How Fast Can SM4 be in Software?

SM4 is a popular block cipher issued by the Office of State Commercial Cryptography Administration (OSCCA) of China. In this paper, we use the bitslicing technique that has been shown as a powerful strategy to achieve very fast software implementations of SM4. We investigate optimizations on multiple frontiers. First, we present an efficient bitsliced representation for SM4, which enables running 64 blocks in parallel with 256-bit registers. Second, we adopt Boyar’s combinational logic optimization method for a more optimal SM4 S-box. The above optimizations contribute to an $${\approx }6$$ times performance gain on one core compared with the state-of-the-art result. As the bitsliced implementation requires a non-standard input/output data form compatible with existing parallel modes of operation, we present the algorithms for data form transformations in different cases, allowing efficient implementations of SM4 under Counter (CTR) mode and Galois/Counter Mode (GCM). Furthermore, since the overhead on (even optimized) data form transformations is non-negligible, we suggest some adjustments of CTR mode and GCM with respect to the bitsliced implementation, resulting in bitslicing-friendly variants thereof.
Teachers Perspectives on Transition to Online Teaching Deaf and Hard-of-Hearing Students during the COVID-19 Pandemic: A Case Study

Education has faced various challenges due to the COVID- 19 pandemic. These challenges were even more dramatic for deaf and hard-of-hearing students as they transitioned to the online setting. This paper aims to explore the teachers’ perspectives on transitioning to educating computer science deaf students at a technical college in the Kingdom of Saudi Arabia (KSA). Therefore, the paper presents the results of 10 surveys and 5 interviews with faculty to identify the main obstacles that teachers have faced during the transition. This study outcomes reveal several challenges related to poor accessibility of learning tools, issues with the online availability of materials, no updates on the teaching pedagogy to accommodate the new settings and communication problems between teachers and students. Our study outlines important takeaways to provide deaf and hard-of-hearing students with a better learning experience.
Extended Ising Machine with Additional Non-quadratic Cost Functions

We have developed a method to extend the functionality of the Ising machine, which currently has an energy function limited to a binary-quadratic form. The proposed method utilizes auxiliary variables dependent on the decision variables of the problem to be solved so that it can be accelerated by hardware parallel processing. Auxiliary variables add third-order or higher terms or rectified linear unit-type nonlinear functions to the binary-quadratic energy function of the Ising machine, modifying the value of the energy difference resulting from the reversal of the decision variable. To enable parallel computation, the computation of the energy change uses information that can be accessed locally by the decision neurons. We confirmed that the proposed method works for the 0/1 linear knapsack problem, the quadratic knapsack problem, the 3-SAT problem, and the random 3-XORST problem.
Load-Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for Next Generation Wireless Systems

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value.
IS-Net: Automatic Ischemic Stroke Lesion Segmentation on CT Images

Ischemic stroke is an acute cerebral vascular disease and makes up about 80% of all stroke cases. Noncontrast computed tomography (NCCT) is a widely applied imaging technique for ischemic stroke assessment. However, it is challenging to identify ischemic lesion on NCCT images due to its high variability in location, contrast, and geometry. In this work, we propose IS-Net, an encoder–decoder convolutional neural network for automatic ischemic stroke lesion segmentation on NCCT images. The proposed IS-Net takes a hierarchical network as backbone while the pyramid feature aggregation (PFA) module is designed to aggregate features from multistages of backbone, and reasonable feature fusion strategy is considered in PFA to enhance multilevel propagation. To fully mine the boundary cues, the edge constraint scheme is introduced by deep supervision which broadcasts low-level features to different modules. In addition, to overcome the limitation of fixed geometric structure of convolution for multirange dependency exploitation, a nonlocal parallel decoder is introduced with deformable convolution and self-attention. The proposed IS-Net is evaluated on manually labeled follow-up NCCT dataset composed of 1004 cases (totally 9020 images). The proposed IS-Net is compared with the state-of-the-art segmentation models and illustrates the highest score on segmentation criteria and sensitivity.
Performance improvement of in-materio reservoir computing by noise injection

Abstract Computation performance of in-materio reservoir device was evaluated by varying intensity of noise injection. Materials for the reservoir device was synthesized using a α -Fe 2 O 3 /titanium bismuth oxide composite by using the sol–gel method. The prepared samples were characterized by conducting X-ray diffractmetry, transmission electron microscopy, and energy dispersive X-ray spectroscopy to confirm presence of α -Fe 2 O 3 , TiO 2 , and Bi 4 Ti 3 O 12 nanoparticles. The I – V and V – t curves show nonlinearity, and phase differences between input and output signals, and the fast Fourier transform of the V – t curve showed high harmonics at the input sine wave with 11 Hz of frequency. In the waveform prediction task, the prediction accuracy was improved only when a small intensity of white noise voltage was superimposed to the input information signal.
The application of health recommender systems based on knowledge graph: a scoping review

Abstract Background: Tailored knowledge graph-based recommender systems (KGRSs) have been demonstrated to be able to provide accurate and effective health recommendations to users, and thus significantly reduce health care costs. They are now strongly recommended to be applied in the health care field. Objective: This scoping review aims to identify the current application of KGRSs, their target users and performance metrics, and the potential limitations of implementing health recommender systems in clinical practice. Methods: A review of the studies published from inception to November 1, 2022 was conducted, using key search terms in 6 scientific databases to identify health recommender systems based on knowledge graph technology. Key information from the included studies was extracted and charted. The scoping review was reported following the PRISMA Extension for Scoping Reviews. Result: We included 16 studies and 5 grants totally about the health recommender systems based on knowledge graph technology. They were used in different health areas: traditional Chinese medicine, health management, disease-related decision support, diet, and nutrition recommendations. Among them, 6 studies were for the general public and 6 were for physicians. A total of 13 (81.25%) studies evaluated the KGRS using performance metrics, such as accuracy, recall, F1 score, and area under the curve. All studies pointed out the limitations of the recommender systems and provided directions for their subsequent optimization and improvement. Conclusion: This review describes the state-of-the-art and potential limitations of KGRS used in the health care field. This novel approach has been proven to be effective in overcoming the drawbacks of traditional algorithms, helping users filter massive amounts of data to find out the personalized information they need. Its great potential in digital health needs to be further explored.
Medical image annotation and classification employing pyramidal feature specific lightweight deep convolution neural network

Medical image annotation has significant potential to detect multiple tags. To detect specific tags and labels, most of the conventional learning algorithms took difficulty in matching the tag with corresponding region in the medical images. Hence, most of the medical image annotations fail to reproduce discrimination between various classes. In this research work to tackle medical image discrimination, we proposed a lightweight pyramidal feature-specific learning deep learning network. The proposed pyramidal feature-specific Lightweight Convolutional Neural Network (LDCNN) architecture for classifying local visual regions to annotate the classified region. By employing pyramidal learning, the proposed LDCNN align each medical image annotation with its region. The colour conversion makes low computation complexity, since the medical image exhibits degeneration by colour absorption. Hence, the interpretability and classification effectiveness increase. To evaluate the effectiveness and classification accuracy, we compare the proposed LDCNN with AlexNet and EfficientNet on benchmark datasets like MS-COCO, LC25000 and multiclass Kather datasets. Empirical experimental performance index obtained by the proposed LDCNN outperforms baseline convolutional neural network architecture. The proposed LDCNN achieves 99.6% accuracy, 98.4% sensitivity, 97.9% specificity and 99.1% F1 score. Hence, there is a steady improvement in medical annotation efforts and classification by our proposed lightweight feature-specific learning network.
IB-TransUNet: Combining Information Bottleneck and Transformer for Medical Image Segmentation

Medical image segmentation plays an important role in disease diagnosis and surgical guidance. There are two problems in the current field of medical image segmentation. First, due to the inherent locality of convolution operations, it is difficult for convolutional neural network models to capture global context information. Second, the data set is usually small and the model is at risk of overfitting. To solve the above problems, we innovatively introduced Transformer and information bottlenecks based on the UNet model (IB-TransUNet). Transformer can capture global context information. Information bottleneck can compress redundant features and reduce the risk of overfitting in medical image segmentation tasks. Furthermore, we add a multi-resolution fusion mechanism to skip connections, which helps high-resolution feature maps to have both spatial texture information and semantic information. Finally, a channel attention block with residuals is added to the decoder to help the model learn relevant features. To verify the performance and efficiency of the proposed model, we conduct ablation experiments on two public datasets and compare them with those state-of-the-art models. Experimental results demonstrate the advantages of the proposed model.
PARMA-CC: A family of parallel multiphase approximate cluster combining algorithms

Clustering is a common task in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g., rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g., detecting critical situations such as detecting boundary crossing from a robot arm that could injure human beings) demand for efficient data clustering algorithms that can effectively utilize the increasing computational capacities of modern hardware. To that end, we leverage approximation and parallelization, where the former is to scale down the amount of data, and the latter is to scale up the computation. Regarding parallelization, we explore a design space for synchronization and workload distribution among the threads. As we study different parts of the design space, we propose representative Parallel Multiphase Approximate Cluster Combining, abbreviated as PARMA-CC, algorithms. We show that PARMA-CC algorithms yield equivalent clustering outcomes despite their different approaches. Furthermore, we show that certain PARMA-CC algorithms can achieve higher efficiency with respect to certain properties of the data to be clustered. Generally speaking, in PARMA-CC algorithms, parallel threads compute summaries associated with clusters of data (sub)sets. As the threads concurrently combine the summaries, they construct a comprehensive summary of the sets of clusters. By approximating a cluster with its respective geometrical summaries, PARMA-CC algorithms scale well with increasing data volumes, and, by computing and efficiently combining the summaries in parallel, they enable latency improvements. PARMA-CC algorithms utilize special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC algorithms can complement and outperform well-established methods, with significantly better timeliness especially when utilizing multiple threads, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour.