Reviewerly/exHarmony huggingface dataset:
Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching (SADM) network for CISDL, consisting of a feature extractor, a scale-adaptive correlation module and a novel mask generator. The feature extractor is built on VGG, which has been reconstructed with atrous convolution. In the scale-adaptive correlation computation module, squeeze-and-excitation (SE) blocks and truncation operations are integrated to process arbitrary-sized images. In the mask generator, an attention-based separable convolutional block is designed to reconstruct richer spatial information and generate more accurate localization results with less parameters and computation burden. Last but not least, we design a pyramid framework of SADM to capture multiscale details, which can increase the detection and localization accuracy of multiscale regions and boundaries. Extensive experiments demonstrate the effectiveness of SADM and the pyramid framework.
A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user’s situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes humans as complex sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user’s situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data continuously streamed in that both affects data processing efficiency and complicates the data semantics. To address the above challenges, we propose a data-driven, situation-aware framework for predictive analysis in smart environments. First, to effectively analyze the sensor data, we employ the Situ-Morphism method to transfer sensor-enabled situation information to vector information. Then we introduce new similarity metrics and implement similarity prediction based on Locality Sensitive Hashing to improve data processing efficiency and effectively handle the data semantics. Experiment results show that the predictive analysis method proposed in this paper can be effective.
Explainable Artificial Intelligence for Human Embryo Cell Cleavage Stages Analysis

Many couples struggle with infertility, and opt for assisted reproductive technology (ART). Selecting the embryo with the highest chance of resulting in pregnancy, is one of the most critical steps of the ART procedure. The implantation potential of an embryo is associated with its morphology which includes assessing cell cleavage stages. Today, the assessment is mainly done manually by visual examination by embryologist and therefore is often subjective. Deep learning (DL) models can be used to automatically classify cell cleavage stages, and make the embryo assessment process efficient, objective. However, it is important that embryologists understand and trust the DL predictions, and in this paper we evaluate the potential of three explainable artificial intelligence (XAI) techniques. First, we compare the two XAI techniques Grad-CAM and LIME to identify important characteristics in embryo images associated with specific embryo cleavage stages. Both approaches identified biologically relevant morphological characteristics, but generally Grad-CAM was more consistent than LIME. Secondly, we suggest an approach on how to use the XAI technique SHAP to identify image characteristics that caused some images to be misclassified. The identified image characteristics had meaningful biological interpretation such as fragmentation. Overall the study demonstrates that DL in combination XAI can be useful in embryo assessment and selection.
Boost-Phase Trajectory Planning with the Nonregular Reachable Area Constraints

During emergency return or mission change, the boost-glide vehicle needs to meet the nonregular reachable area constraints (NRACs). Therefore, this paper introduced NRACs into boost-phase trajectory planning to extend the maneuvering range and enhance the mission adaptability of boost-glide vehicle. Firstly, the library of the reachable area boundaries was constructed with the fast computation method under the deviations of boost terminal states, and the polynomials were used to fit the boundary parameters. Secondly, the nonlinear mapping relationship between the reachable area boundary parameters and the deviations of boost terminal states was obtained by the deep neural networks (DNNs). Then, the new boundary parameters were obtained by the constraint transformation rules under NRACs and the separation window constraints and transformed into the constraints of the boost-phase terminal states by DNNs. Finally, the hp-adaptive pseudospectral method (hpPM) was adopted to complete the trajectory planning considering the path and terminal constraints. The simulation results showed that the proposed trajectory planning method considering NRACs had high trajectory planning accuracy and good deviation adaptability and exhibited excellent performance in the adjustment of reachable area. This study provides theoretical support for integrate mission decisions and trajectory planning of boost-glide vehicles.
Visualize the 3D Virtual Model Through Augmented Reality (AR) Using Mobile Platforms

The Architecture, Engineering, and Construction (AEC) industry is amongst the oldest. Developments to transform the virtual models and visualize them in the real world are the ongoing innovations. The AEC industry often faces problems in communicating ideas, which may look vague or cannot come up with the words to describe them between the client and the work executers; errors during construction due to lack of imagination; improper planning and execution; and wastage of resources. To address the lack of imagination or visualization of elements in architecture, an effort has been made by creating virtual 3D models like chairs, tables, office cabins, and furnished house structures. The elements are exported to a game environment to create an application that can be used on the Android platform. This in turn helps the user to visualize the 3D model with details and proactively rectify the errors. The app development kit includes Unity 3D and Vuforia. This paper focuses on the development of an application for the visualization of virtual 3D models augmented on the Android mobile platform.
A CNN Hardware Accelerator Using Triangle-based Convolution

Convolutional neural networks (CNNs) have gained a massive impression in the fields of computer vision and especially in the embedded applications because of their high accuracy and performance. However, high computational complexity and power consumption due to convolution operations causes a high demand for low-power accelerators. A 3D geometric optimization strategy is proposed to alleviate the area and power requirements of Multiply Accumulate operations prevalent in all spatial CNNs. The proposed technique is generic and may be easily scaled for accelerators performing spatial 2D convolution.
Adaptive Bilateral Texture Filter for Image Smoothing

The biggest challenge of texture filtering is to smooth the strong gradient textures while maintaining the weak structures, which is difficult to achieve with current methods. Based on this, we propose a scale-adaptive texture filtering algorithm in this paper. First, the four-directional detection with gradient information is proposed for structure measurement. Second, the spatial kernel scale for each pixel is obtained based on the structure information; the larger spatial kernel is for pixels in textural regions to enhance the smoothness, while the smaller spatial kernel is for pixels on structures to maintain the edges. Finally, we adopt the Fourier approximation of range kernel, which reduces computational complexity without compromising the filtering visual quality. By subjective and objective analysis, our method outperforms the previous methods in eliminating the textures while preserving main structures and also has advantages in structure similarity and visual perception quality.
Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.
Approximate Methods for Solving Problems of Mathematical Physics on Neural Hopfield Networks

A Hopfield neural network is described by a system of nonlinear ordinary differential equations. We develop a broad range of numerical schemes that are applicable for a wide range of computational problems. We review here our study on an approximate solution of the Fredholm integral equation, and linear and nonlinear singular and hypersingular integral equations, using a continuous method for solving operator equations. This method assumes that the original system is associated with a Cauchy problem for systems of ordinary differential equations on Hopfield neural networks. We present sufficient conditions for the Hopfield networks’ stability defined via coefficients of systems of differential equations.
An Improved Pity Beetle Algorithm for Solving Constrained Engineering Design Problems

To cope with increasingly complex models of engineering design problems and to obtain more accurate design solutions, this paper proposed an improved population-based, bio-inspired optimization algorithm, called the pity beetle algorithm based on pheromone dispersion model (PBA-PDM). PBA-PDM enables a local and global search for optimization problems through the pheromone release mechanisms in female beetles and the interaction relationship between male beetles. The experimental results compared with other state-of-the-art metaheuristic optimization algorithms show that PBA-PDM has an ideal performance when dealing with both classical test functions and CEC2017 benchmark test functions. Then, the PBA-PDM is applied in dealing with real-world constrained engineering design problems to verify the effectiveness and applicability. The above experimental results show that the PBA-PDM proposed in this paper is an effective and efficient algorithm for solving real-world optimization problems.
A Study on Big Data Privacy in Cross-Industrial Challenges and Legal Implications

Data privacy (also known as information privacy or data protection) refers to the legal right of the data subject to access, use, and collection of data. For the development of a digital economy, privacy protection and identity management are critical. The current regulatory framework's efficiency has been called into consideration from a legal standpoint. In both industry and academia, big data has become a hot issue for research. This chapter presents a succinct but comprehensive examination of data security and privacy issues at all phases of the data life cycle. The chapter then goes over some of the current solutions. Finally, this chapter discusses future research projects related to data security and privacy protection. It further explores other facets of the privacy problem that turn out to be critical for the broad adoption of privacy-enhancing technology, giving a more holistic picture of the situation.
Robust coincidence Bell inequalities for the noisy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -qutrit Greenberger-Horne-Zeilinger state

The general form of the coincidence Bell inequalities for an arbitrary $(n,2,3)$ scenario [i.e., an $n$-party, two-setting, and three-dimensional system (qutrit) scenario] is presented. To detect the nonlocal properties of the noisy $n$-qutrit Greenberger-Horne-Zeilinger states, in this work we investigate the most robust (4,2,3)-scenario and (5,2,3)-scenario coincidence Bell inequalities. By deriving the most robust $(n\ensuremath{-}1)$-party coincidence inequalities, we have established two tight and inequivalent $(n,2,3)$ coincidence Bell inequalities with a visibility of 0.5 for $n=4$ and, similarly, two tight and inequivalent $(n,2,3)$ coincidence Bell inequalities with a visibility of 0.488785 for $n=5$. These inequalities are all tight. To our knowledge, up to now these inequalities have been the most robust Bell inequalities for the corresponding scenarios. Our results are useful for building the iteration formula of $(n,2,3)$-scenario coincidence Bell inequalities with the lowest critical visibility.
Unlocking IoT

Meeting ESG (environmental, social, and governance) standards is becoming an essential goal for businesses, one that is supported by both investors and customers. FinTech can help companies evaluate and reduce their environmental effect, as well as investors channel their operations toward more sustainable assets. Embracing sustainability has become one of the priorities for many fintech companies today. Green fintech innovations have provided technologically enabled solutions for financial services that help in increasing the flow of financial resources for sustainable development. Recognizing the importance of green fintech in today's scenario, this chapter will initially focus on the components of the fintech ecosystem and IoT and AI-enabled fintech innovations in India, drivers of green fintech potentials of green fintech IoT, and AI-enabled green fintech from global perspective and has highlighted the green fintech solutions provided by the topmost companies.
Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.
Security Monitoring Strategy of Distribution Community Operation Site Based on Intelligent Image Processing Method

With the continuous development of energy infrastructure, a large number of distributed new energy, distributed energy storage, and various power electronic equipment are connected to distribution communities. The access to a large amount of equipment not only increases the workload of power operators, but also leads to a complex field operation environment, which will threaten the security of field operators. Traditional monitoring strategy for distribution community operation site relies on manual operation, which is tedious, labor-intensive, and error-prone. To solve this problem, this paper proposes a security monitoring strategy for distribution community operation sites based on an intelligent image processing method. Firstly, a power image enhancement method based on multi-filtering algorithm is proposed. The filter operators are used to smooth and denoise the image, enhance the edge and detail information, and expand the security monitoring data set of operation sites by merging the filtered images. Secondly, an object detection method for personnel and security protection tools based on the improved Faster R-CNN algorithm is proposed, and a pyramid structure is constructed to improve the detection and localization accuracy of small objects such as safety helmets and safety belts. To simplify the object detection task and the complexity of the labeling system, it is disassembled into four single tasks: operators, non-operators, safety helmets, and safety belts. Finally, the detection frame labels are fused by calculating the overlap of the object detection boxes to describe the security of the operator. Experiments show that the method proposed in this paper can effectively locate the object and accurately describe the security of the operator.
Acceptance of Remote Education During COVID-19 Outbreak in Undergraduate Nursing Students

This study aimed to examine the acceptance and predictors of remote education through Internet-based learning among undergraduate nursing students in Jordan. An online survey was used with a sample of 344 students to assess satisfaction with remote education. Responses indicated that undergraduate nursing students were unsatisfied with remote education for several reasons. Many students (n = 188, 55 percent) strongly agreed that problems and obstacles were encountered when they studied subjects electronically. The acceptance of remote education was predicted by educational level (p = .01), device used (p = .001), and Internet reliability p = .001).
A Neural Network Model Secret-Sharing Scheme with Multiple Weights for Progressive Recovery

With the widespread use of deep-learning models in production environments, the value of deep-learning models has become more prominent. The key issues are the rights of the model trainers and the security of the specific scenarios using the models. In the commercial domain, consumers pay different fees and have access to different levels of services. Therefore, dividing the model into several shadow models with multiple weights is necessary. When holders want to use the model, they can recover the model whose performance corresponds to the number and weights of the collected shadow models so that access to the model can be controlled progressively, i.e., progressive recovery is significant. This paper proposes a neural network model secret sharing scheme (NNSS) with multiple weights for progressive recovery. The scheme uses Shamir’s polynomial to control model parameters’ sharing and embedding phase, which in turn enables hierarchical performance control in the secret model recovery phase. First, the important model parameters are extracted. Then, effective shadow parameters are assigned based on the holders’ weights in the sharing phase, and t shadow models are generated. The holders can obtain a sufficient number of shadow parameters for recovering the secret parameters with a certain probability during the recovery phase. As the number of shadow models obtained increases, the probability becomes larger, while the performance of the extracted models is related to the participants’ weights in the recovery phase. The probability is proportional to the number and weights of the shadow models obtained in the recovery phase, and the probability of the successful recovery of the shadow parameters is 1 when all t shadow models are obtained, i.e., the performance of the reconstruction model can reach the performance of the secret model. A series of experiments conducted on VGG19 verify the effectiveness of the scheme.
Image haze removal and computer-generated holographic display method in coal mine

This paper presents a method of haze removal and computer-generated holographic display of degraded images in coal mine. Firstly, the image enhancement of underground coal mine is realized by using the dark channel prior haze removal algorithm, which greatly weakens the shielding of coal dust and water mist in the roadway environment. Next, using the computer-generated hologram algorithm based on angular spectrum diffraction, the phase only hologram is generated with the haze removal image as the input. The peak signal-to-noise ratio (PSNR) of the reconstructed image of the red channel is 65.47dB, the PSNR of the reconstructed image of the green channel is 64.98dB, the PSNR of the reconstructed image of the blue channel is 65.78dB, and the average PSNR is 65.41dB. The simulation results show that high-quality reconstructed image can be obtained by combining dark channel prior and computer-generated hologram, and the image enhancement of underground coal mine is realized.