Reviewerly/exHarmony huggingface dataset:
Improved speeded-up robust features registration method based on particle swarm and gray wolf algorithm for coal mine images

Image registration is an electronic imaging technology that matches two or more images acquired at different times or under different conditions. Here, we highlight the application of image registration in monitoring robots (e.g., autonomous vehicles) of coal mines. In image registration processing, the scale-invariant feature transform algorithm and its improved versions achieve poor extraction effects for feature points on smooth-edge curves. In comparison, the speeded-up robust features (SURF) algorithm achieves more stable registration results in various images. To improve the accuracy of the SURF algorithm, it is optimized using the particle swarm optimization (PSO) algorithm and several improved PSOs, but it achieves unstable accuracy. Therefore, we optimize the improved gray wolf algorithm (IGWO) using advanced mathematical models in several computing steps. We add the memory function of both experienced individual and group optimal locations in PSO to update the particle positions. The evaluations performed on different datasets show that the accuracy and range of registration are substantially improved by IGWO and IGWO-PSO; however, the running time is extended. Thus, our improved method is competitive with other state-of-the-art image registration methods.
An adversarial transferability metric based on SVD of Jacobians to disentangle the correlation with robustness

Abstract Transferability of adversarial samples under different convolutional neural network (CNN) models is one of the metrics indicators to assess the efficiency of adversarial examples and an important research direction in defense of that. Transferability isolate models employ a particular alternative model to avoid black-box attacks. Meanwhile, recent research has revealed that adversarial transferability across sub-models may be utilized to express the diversity requirements of sub-models under ensemble robustness abstractly. Due to the lack of mathematical description for this adversarial transferability, it was utilized to be abstractly described as the diversity of different hypotheses. This paper employs the Jacobians matrix’s singular value decomposition (SVD) to provide a more accurate mathematical description of the transferability of adversarial samples between models and proposes a corresponding evaluation metric. Based on this metric, a new regularization constraint is introduced into the ensemble training, and the adversarial transferability between the sub-models is isolated optimally without the prior information of the adversarial samples. Based on the proposed metric accurately defining the transferability, further ensemble robustness experiments under small-scale dataset disentangle the correlation between transferability and robustness, indicating that the transferability isolation can only achieve robustness under an alternative transfer-based attack with partial sub-models of the ensemble.
Haze optical-model-based nighttime image dehazing by modifying attenuation and atmospheric light

Scene imaging is often affected by artificial light sources within a hazy environment at night, causing degraded images with low brightness, color distortion, and glow. These problems render the traditional atmospheric scattering optical model obsolete and incompatible. To address this issue, we established an optical imaging model suitable for nighttime dehazing, and an illumination component is incorporated into the attenuation term. We also introduced the near-light source coefficient to redefine the glow. Based on this model, we propose a new nighttime dehazing method. First, the rough atmospheric light is estimated using its low-frequency characteristics. Then, the glow is calculated by the near-light source coefficient. Finally, we remove the haze and illumination to get a clear image. Extensive experiments prove that our method exhibits a better color recovery effect, which effectively improves the visibility and detail. Furthermore, we believe our method outperforms other methods, both qualitatively and quantitatively.
The Force Unleashed

Was the rise of humankind inevitable? This chapter discusses a sequence of filters that the human civilization has passed to achieve the current level of development. It emphasizes the importance of crossing the threshold of net positive cumulative knowledge accumulation, underlying the rise of the homo sapiens as the dominant species on Earth. Human local control maximization is an emergent sub-routine of the grand process of species evolution which got out of evolutions hand. The chapter also speculates whether artificial general intelligence (AGI) will become the next filter for our civilization. There are at least two reasons why it may be. First, the orthogonality thesis: any level of intelligence can be coupled with more or less any final goal. Second, the sheer power that comes with intelligence. The AGI optimization process, a sub-routine of human local control maximization, may get out of hand just like human local control maximization got out of evolutions hand—through its pace, recursive self-improvement and greed for resources.
Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement

Purpose Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy. Design/methodology/approach To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress. Findings The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance. Originality/value The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.
Bringing a background variable into focus: a multimethod investigation of IT security climate’s influence on elicited IT security beliefs

Purpose IT security breaches plague organizations worldwide, yet there continues to be a paucity of comprehensive research models for protective technologies. This study aims to develop an IT security user behavior model focusing on the protective technology anti-spyware which includes organizational climate, a theory of planned behavior (TPB) background variable and elicited salient user beliefs. Design/methodology/approach A multimethod approach, including interviews and a survey, is used to elicit salient user beliefs and test hypotheses of the influences of perceived IT security climate on those user beliefs and ultimately user behavioral intentions. Primary data were collected through interviews following the prescribed TPB methodology and an offline survey method with 254 valid responses recorded. Partial least squares was used to investigate the hypotheses. Findings The authors found that attitudinal beliefs – protecting organizational interests for data/privacy, preventing disruptions to work and control beliefs – monetary resources and time constraints mediate significant relationships between IT security climate and attitude and perceived behavioral control, respectively. Implications are discussed. Originality/value This study is the first, to the best of the authors’ knowledge, that uses both interviews and a survey to examine the relationships among IT security climate, elicited user beliefs and behavioral intentions in a TPB-based model for a protective technology.
US PUBLISHING AS A CULTURAL PHENOMENON: HISTORY, CURRENT REALITIES AND PROSPECTS

The article analyses the specifics of the formation of the American publishing system in the context of the incipience of national culture and statehood. It highlights the importance of print production as an important component of the overall system for the formation and maintenance of the original face of the United States – both in the early stages of the formation of the state and culture, as well as in modern realities, complicated by the Covid pandemic situation. The US publishing system is at the forefront of technological changes that affect both American and world reading and book culture: not only the diversity and democratisation of the form of consumer culture, but also the quality of the media sphere as a special space for the life of a modern person. The experience of the American book market, the US publishing industry is considered, demonstrating the importance of publishing as a meaningful tool for socialisation and inculturation, its significant influence on the formation of national traditions of "bookishness", reading culture, and on the level of the general cultural education of the nation.
On the performance of SQL scalable systems on Kubernetes: a comparative study

Abstract The popularization of Hadoop as the the-facto standard platform for data analytics in the context of Big Data applications has led to the upsurge of SQL-on-Hadoop systems, which provide scalable query execution engines allowing the use of SQL queries on data stored in HDFS. In this context, Kubernetes appears as the leading choice to simplify the deployment and scaling of containerized applications; however, there is a lack of studies about the performance of SQL-on-Hadoop systems deployed on Kubernetes, and this is the gap we intend to fill in this paper. We present an experimental study involving four representative SQL scalable platforms: Apache Drill, Apache Hive, Apache Spark SQL and Trino. Concretely, we analyze the performance of these systems when they are deployed on a Hadoop cluster with Kubernetes by using the TPC-H benchmark. The results of our study can help practitioners and users about what they can expect in terms of performance if they plan to use the advantages of Kubernetes to deploy applications using the analyzed SQL scalable platforms.
X-Net: A Novel Deep Learning Architecture with High-resolution Feature Maps for Image Segmentation

Brain tumor segmentation is a critical task in diagnosing brain cancer and highly challenging due to the wide variety of the tumors’ location, size, and shape. Deep learning has been widely employed in the automatic segmentation of brain MRI images. There are a number of deep learning architectures proposed for seminal image segmentation with different levels of success. In general, the accuracy of the deep learning models depends on the size of the training dataset and the complexity of the model. In this paper, we proposed a novel deep learning architecture with relatively low complexity for the segmentation of brain tumors from MR images. Most of the deep learning architectures reduce the size of feature maps using techniques such as max-pooling and adding several convolutional layers. In the proposed architecture (called X-Net), the feature maps are up-sampled using transposed convolution in the first place to encode details of the input image more precisely (relying on the idea of high-resolution focusing on the details and complex structures such as camera lenses). We evaluated the proposed X-Net model using a challenging dataset for brain tumor segmentation from MR images (BraTS 2020). To verify the performance of the X-Net model, two conventional Unet models with low parameters (simple) and high parameters (complex) were also implemented. The quantitative evaluation demonstrated the superior performance of the proposed model with less trainable parameters. The X-Net model (with 0.63 M parameters) achieved a Dice score of 0.72 compared to the simple (0.54 M parameters) and complex (33.54 M parameters) Unet models with Dice indices of 0.50 and 0.67, respectively. The proposed model could be employed for the segmentation tasks with high accuracy.
Prediction Error Propagation: A Novel Strategy to Enhance Performance of Deep Learning Models in Seminal Segmentation

Segmentation of tumor and healthy surrounding organs, known as organs at risk (OAR), plays a key role in accurate diagnosis and treatment planning. To this end, many automated methods have been proposed, especially using deep learning methods. Convolutional neural networks (CNNs), inspired by the human visual cortex, entail the feedforward connections, however, the feedback connections from higher levels/layers to lower levels/layers in the visual cortex are missing in these models. These feedback connections are intended to carry the prediction errors of incoming information for the next moment/step to minimize the overall errors between reality and the prediction in the visual cortex. Inspired by the visual cortex structures, these feedback connections were proposed to be considered into CNN models in the form of prediction error propagation (PEP). To this end, a novel framework has been proposed that consists of three parts; encoder, reconstruction, and segmentation parts, wherein the input image is passed through encoder and reconstruction parts to create an error map of the predicted information/images (similar to what happens in visual cortex). This prediction error map is then propagated/feedbacked to the encoder part to aid accurate segmentation. The proposed PEP framework was implemented on a conventional Unet model (PEP-Unet). The PEP-Unet was trained for a multi-organ segmentation task from CT images (SegTHOR public dataset). The performance of the PEP-Unet was compared with the conventional Unet architecture using the standard segmentation indices. The proposed PEP framework led to an overall 11% improvement in terms of the Dice index. This study demonstrated the effectiveness of the PEP framework for deep learning-based seminal segmentation.
Training with Probability Map: an Effective Framework for Deep Learning Training with Multi-observer Labeled Datasets

The substantial inter-observer variability makes grading prostate tissue micro-array (TMA) cores to determine Gleason scores difficult. Due to the huge variance in pathologists' scores and the lack of proven ground truth, developing a deep learning-based model for automated grading of prostate TMAs is extremely difficult. In light of this, a number of ways for training deep learning algorithms with noisy or multi-observer labeled datasets have been developed. This paper evaluates a number of widely used and effective methods for training a model with a heterogeneous dataset. In addition, deep learning model training with a probability map is presented as a solution to this problem. To test the alternative training procedures, 240 prostate TMA cores from the Gleason2019 challenge (evaluated independently by six pathologists with Cohen's Kappa agreement coefficients between 0.40 and 0.73) were used. Training with single pathology scores, training with a single label map generated by majority voting (MV) and STAPLE methods, training the model with L1-norm and L2-norm loss functions using the entire scores of pathologists, and training with a single probability map obtained from the entire scores of pathologists are among the strategies evaluated in this study. The ground truth was the label map created by STAPLE. The model trained with the proposed probability map had the best accuracy of 0.93, whereas the models trained with the L2-norm and STAPLE had accuracy of 0.89 and 0.86, respectively. In comparison to these models, other approaches produced poor results. In comparison to the commonly utilized methodologies, the proposed simple training strategy yielded the highest accuracy. A multi-observe labeled dataset could benefit from the proposed approach for decision fusion and ground truth estimation.
An Investigation for Colorectal Cancer Early Diagnosis Using Hessian Vector-based Texture Features

Early screening and diagnosis for polyps will significantly reduce their cancerous likelihood since most colorectal cancers are stemmed from neoplastic polyps at their earlier stage. This paper attempted to investigate the accuracy rate of diagnosis on small and medium polyps. To mitigate the bias of this study, a two-step-two-user scheme was adopted to simulate the real scenario in our practice. Two users are required to anticipate the investigation for two different missions, i.e. training and prediction, where Hessian vector is employed to generate texture features. To further study the effect of different growing stages, three training schemes are designed to carry out our investigations, i.e. small-polyp-mode, medium-polyp-mode and mixed-polyp-mode. Experimental outcomes demonstrate that the medium-polyp-mode could bring the best results while the worst results were produced over the small-polyp-mode. By considering the small sizes of polyps in their earlier stage, the results of medium-polyp-mode and the mixed-polyp-mode are acceptable.
Super-resolution PET Brain Imaging using Deep Learning

Positron emission tomography (PET) is a noninvasive and reproducible way of medical diagnostic technology. However, due to its intrinsic imaging characteristics, the PET image resolution is inferior compared to other medical imaging modalities. PET image quality and quantitative accuracy are degraded due to its poor resolution. In this study, we present a super-resolution (SR) approach for PET images based on a deep learning network. The network was trained on images from the high-resolution research tomograph (HRRT) scanner and its blurred low-resolution (LR) counterparts matching the clinical mCT scanner resolution. Data augmentation methods were also applied to improve the training data’s generalizability. After the training, the model was validated using the blurred LR HRRT images. Also, the proposed network was applied to enhance the resolution of routine clinical brain images acquired on an mCT scanner. The results reveal that the proposed approach leads to a substantial improvement in PET image resolution and overall quality.
Implementation and Performance Analysis of Lightweight Block Ciphers for IoT applications using the Contiki Operating system

Recent years have witnessed impressive advances in technology which led to the rapid growth of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) using numerous low-powered devices with a huge number of actuators and sensors. These devices gather and exchange data over the internet and generate enormous amounts of data needed to be secured. Although traditional cryptography provides an efficient means of addressing device and communication confidentiality, integrity, and authenticity issues, it may not be appropriate for very resource-constrained systems, particularly for end-nodes such as a simply connected sensor. Thus, there is an ascent need to use lightweight cryptography (LWC) providing the needed level of security with less complexity, area and energy overhead. In this paper, four lightweight cryptographic algorithms called PRESENT, LED, Piccolo, and SPARX were implemented over a Contiki-based IoT operating system, dedicated for IoT platforms, and assessed regarding RAM and ROM usage, power and energy consumption, and CPU cycles number. The Cooja network simulator is used in this study to determine the best lightweight algorithms to use in IoT applications utilizing wireless sensor networks technology.
Vehicle Recognition Combining ResNeXt Network and Label Smoothing Regularization

The commonly used cross-entropy loss function only calculates the loss for the correct position of the label, but ignores the influence of the wrong label. This makes the model too confident, resulting in lower prediction accuracy during model testing than training. We combine the ResNeXt network model with the label smoothing regularization method. Our model for fine-grained recognition of vehicle can improve the accuracy of vehicle recognition.
Hierarchical Cluster Analysis Implementation Using the Algorithm of Clustering Using Representatives

Data mining is an important concept in data collection because it is used to evaluate a large amount of data, whether formal or informal, and starts with numerous goals, the first of which is to analyze and understand the data and finally to predict future behavior and make informed judgments. Where the work on the site begins and you find invisible or inexplicable patterns and get the key information that can predict the right thing., classification and final integration, and each of these approaches involves a set of algorithms.
A Traffic Classification Method for Internet Application Services Based on DNN

With the development of information technology, it is an important responsibility of internet service providers to meet the demands of various users for Internet application services. However, to provide various application services with good Quality of Service (QoS), an effective, accurate, and real-time network traffic classification mechanism is required. However, under consideration of information security, most Internet traffic is currently transmitted in an encrypted way, making the traditional method of packet field detection unable to effectively classify network traffic. Therefore, the paper proposes an Internet application service traffic classification method based on the DNN model to solve the problem that encrypted traffic is not easy to classify. The experimental results show that the proposed method has a good performance in precision, recall, and the F1 score, and the overall classification accuracy of the system can reach 0.91.
Multimedia Objects and Forensic Determinations of Criminal Responsibility

Multimedia objects may be used in adjudicative fora as direct or circumstantial evidence along the evidentiary spectrum. There are risks under US and Russian law to justice in the analysis of multimedia objects. This are issues for everyone in an electronic world.
Secured Monitoring of Smart Grid Assets using Attribute based Encryption and Blockchain

A smart grid network involves various assets which are enabled to communicate for different operations such as energy exchange, maintenance and operational data management. These assets need to be securely monitored and maintained in real time. In addition, information from these assets should be securely communicated. In this paper, we propose a security system that enables smart grid assets to securely communicate different maintenance and operational alerts so that utility companies can take appropriate action based on the received alert in a time. It uses ciphertext policy-attribute based encryption for securely communicating the alert messages and recording them on blockchain to offer alert data security and immutability. The security strength of the proposed system is analyzed by considering the selective-set model and collision resistance property.