Reviewerly/exHarmony huggingface dataset:
Partially Deep-Learning Encryption Technique

The biggest problem facing the world is information security in the digital era.Information protection and integrity are hot topics at all times, so many techniques have been introduced to transmit and store data securely.The increase in computing power is increasing the number of security breaches and attacks at a higher rate than before on average.Thus, a number of existing security systems are at risk of hacking.This paper proposes an encryption technique called Partial Deep-Learning Encryption Technique (PD-LET) to achieve data security.PD-LET includes several stages for encoding and decoding digital data.Data preprocessing, convolution layer of standard deep learning algorithm, zigzag transformation, image partitioning, and encryption key are the main stages of PD-LET.Initially, the proposed technique converts digital data into the corresponding matrix and then applies encryption stages to it.The implementation of encrypting stages is frequently changed.This collaboration between deep learning and zigzag transformation techniques provides the best output result and transfers the original data into a completely undefined image which makes the proposed technique efficient and secure via data encryption.Moreover, its implementation phases are continuously changed during the encryption phase, which makes the data encryption technique more immune to some future attacks because breaking this technique needs to know all the information about the encryption technique.The security analysis of the obtained results shows that it is computationally impractical to break the proposed technique due to the large size and diversity of keys and PD-LET has achieved a reliable security system.
Detection of Copy-Move Forgery in Digital Images Using Singular Value燚ecomposition

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition (SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step, the SVD is determined.First, the diagonal matrix's maximum value (norm) is selected (representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating the matrix or scaled).Then, the similar norms are grouped, and each leading group is separated into many subgroups (elements of each subgroup are neighbors) according to 8-adjacency (the subgroups for each leading group must be far from others by a specific distance).After that, a weight is assigned for each subgroup to classify the image as forgery or not.Finally, the F1 score of the proposed system is measured, reaching 99.1%.This approach is robust against rotation, scaling, noisy images, and illumination variation.It is compared with other similar methods and presents very promised results.
Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

Neural networks play a significant role in the field of image classification.When an input image is modified by adversarial attacks, the changes are imperceptible to the human eye, but it still leads to misclassification of the images.Researchers have demonstrated these attacks to make production self-driving cars misclassify Stop Road signs as 45 Miles Per Hour (MPH) road signs and a turtle being misclassified as AK47.Three primary types of defense approaches exist which can safeguard against such attacks i.e., Gradient Masking, Robust Optimization, and Adversarial Example Detection.Very few approaches use Generative Adversarial Networks (GAN) for Defense against Adversarial Attacks.In this paper, we create a new approach to defend against adversarial attacks, dubbed Chained Dual-Generative Adversarial Network (CD-GAN) that tackles the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative oversampling and undersampling using GANs.CD-GAN is created using two GANs, i.e., CDGAN's Sub-Resolution GAN and CDGAN's Super-Resolution GAN.The first is CDGAN's Sub-Resolution GAN which takes the original resolution input image and oversamples it to generate a lower resolution neutralized image.The second is CDGAN's Super-Resolution GAN which takes the output of the CDGAN's Sub-Resolution and undersamples, it to generate the higher resolution image which removes any remaining perturbations.Chained Dual GAN is formed by chaining these two GANs together.Both of these GANs are trained independently.CDGAN's Sub-Resolution GAN is trained using higher resolution adversarial images as inputs and lower resolution neutralized images as output image examples.Hence, this GAN downscales the image while removing adversarial attack noise.CDGAN's Super-Resolution GAN is trained using lower resolution adversarial images as inputs and higher resolution neutralized images as output images.Because of this, it acts as an Upscaling GAN while removing the adversarial attak noise.Furthermore, CD-GAN has a modular design such that it can be prefixed to any existing classifier without any retraining or extra effort, and 2542 CMC, 2023, vol.74, no.2 can defend any classifier model against adversarial attack.In this way, it is a Generalized Defense against adversarial attacks, capable of defending any classifier model against any attacks.This enables the user to directly integrate CD-GAN with an existing production deployed classifier smoothly.CD-GAN iteratively removes the adversarial noise using a multi-step approach in a modular approach.It performs comparably to the state of the arts with mean accuracy of 33.67 while using minimal compute resources in training.
Federation Boosting Tree for Originator Rights Protection

The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework.It enables model training without having to centralize all data in a central storage point.In the current horizontal federated learning scheme, each participant gets the final jointly trained model.No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model.Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm, each participant will obtain the final jointly trained model.RPBT can guarantee that the local data of the participants will not be leaked, while the final jointly trained model cannot be obtained.It is worth mentioning that, from the perspective of the participants, the scheme uses the batch idea to make the participants participate in the training in random batches.Therefore, this scheme is more suitable for scenarios where a large number of participants are jointly modeling.Furthermore, a small number of participants will not actually participate in the joint training process.Therefore, the proposed scheme is more secure.Theoretical analysis and experimental evaluations show that RPBT is secure, accurate and efficient.
Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue, this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel's quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety of MRI scan datasets of variable quality.For MRI scans, the BRISQUE "blind/referenceless image spatial quality evaluator" and the NIQE "natural image quality evaluator" measures were 39.38 and 3.58, respectively.The proposed image enhancement model, according to the data, produces the best image quality ratings, and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.
SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively.One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections.However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases.In this paper.we consider the ResNet with Partial Convolution based Padding (PCP) instead of ZP to solve SR problem.Since training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities, patch image based SR performance is compared with single full image based one.The experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.
Pixel-Level Feature Extraction Model for Breast Cancer Detection

Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment.The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not.However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them.Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA).The spatial features of the images were extracted using various filters, and the most prevalent ones are selected using the GA and fed into the classifiers for pixel-level categorization.Three classifiers-random forest (RF), decision tree (DT), and extra tree (ET)were used in the proposed model.The parameters of all models were separately tuned, and their performance was tested.The results show that the features extracted by using the GA+PCA in the proposed model are influential and reliable for pixel-level classification in service of the image annotation and tumor identification.Further, an image from benign, malignant, and normal classes was randomly selected and used to test the proposed model.The proposed model GA-PCA-DT has delivered accuracies between 0.99 to 1.0 on a reduced feature set.The predicted pixel sets were also compared with their respective ground-truth values to assess the overall performance of the method on two metrics-the universal image quality index (UIQI) and the structural similarity index (SSI).Both quality measures delivered excellent results.
Information Extraction Based on Multi-turn Question Answering for燗nalyzing Korean Research Trends

Analyzing Research and Development (R&D) trends is important because it can influence future decisions regarding R&D direction.In typical trend analysis, topic or technology taxonomies are employed to compute the popularities of the topics or codes over time.Although it is simple and effective, the taxonomies are difficult to manage because new technologies are introduced rapidly.Therefore, recent studies exploit deep learning to extract pre-defined targets such as problems and solutions.Based on the recent advances in question answering (QA) using deep learning, we adopt a multi-turn QA model to extract problems and solutions from Korean R&D reports.With the previous research, we use the reports directly and analyze the difficulties in handling them using QA style on Information Extraction (IE) for sentence-level benchmark dataset.After investigating the characteristics of Korean R&D, we propose a model to deal with multiple and repeated appearances of targets in the reports.Accordingly, we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined knowledge.We show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.
An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM).When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle.In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.
Autonomous Cooperative Guidance Strategies for Unmanned Aerial Vehicles During On-Board Emergency

This paper presents autonomous guidance strategies for both solo and group of unmanned aerial vehicles (UAVs), to be used when emergency conditions are encountered during mission. Fixed-wing UAVs equipped with batteries that serve as on-board power source are considered. They are also equipped with inertial and supervisory satellite-based navigation system. An emergency condition where limited available on-board power along with nonavailability of both ground command link and satellite-based navigation information is considered. We proposed two guidance strategies, look-lock-land and inject-contribute-exit for single- and multiple-UAV missions. An optimization model is developed to arrive at the maximum area that can be covered by spiral trajectory without violating on-board power constraints. In addition, we also proved that spiral trajectory acts like a circular highway for multiple-UAV missions. Algorithms to achieve look-lock-land, inject-contribute-exit and to compute Inertial Navigation Systems (INS) error correction factor are discussed. The performance of the proposed autonomous emergency guidance strategies is demonstrated on a sea-based oil-well-monitoring mission.
In Favor of Logical Form

Abstract This paper is intended to address the work “Against logical form”, authored by Johnson-Laird in 2010. In it, based on the theory of mental models, Johnson-Laird claims that the way people interpret sentences in natural language has nothing to do with logic. This is because that action is not related to logical forms. According to him, the mental activity is mainly linked to semantics and pragmatics. However, here, following arguments provided by López-Astorga, I try to show that the theory of mental models can be linked to syntactic structures. My main point is made by dealing with an argument given in the mentioned Johnson-Laird’s paper. In principle, that argument seems to undermine proposals such as that of López-Astorga. However, the present work proposes otherwise.
INFLUENCE OF COMPUTER GAMES ON HUMAN PHYSIOLOGICAL FUNCTIONS

Video games are popular leisure activity for all ages and therefore it is needed to examine their psychological and physiological effects. The aim of the presented paper is to point out possible changes in physiological functions due to playing computer games and to present them in a pilot study. Part of the work is the categorization of video games and a systematic overview of studies aimed at examining and confirming or refute their connection. The purpose of this pilot study is to measure blood pressure and human heart rate while playing different video games and to evaluate possible changes in the game. The methodology of the experiment includes demographic data, measured values during the monitoring of individual subjects and the measuring means used.
Insolation Forecasting by using Image-processed Satellite Images Data and Forecasting of Cloud Imageries

Recently, there is a movement aimed at realizing distributed energy systems, especially photovoltaic power generation (PV) is expected in Japan, and it is expected that the number of interconnections of PV will increase in the future. Insolation is easily affected by the weather and the supply of electricity becomes unstable. Therefore, it is important to forecast the amount of insolation. In this study, the main purpose of this research is to develop a highly accurate and practical method for insolation forecasting, based on satellite image data provided by the Himawari-8 satellite and past actual measurement data released by the Japan Meteorological Agency. In this paper, the authors propose an amount of insolation forecasting method by using neural network and carry out the accuracy verification by case study. In addition, the authors also use the ConvLSTM (Convolutional Long Short-Term Memory) model to forecast the cloud imageries for the insolation forecasting. In order to verify the usefulness of the proposed method, the forecast results by the proposed method were compared with the forecast results without using satellite image data and the forecast results with latest satellite image data. In conclusion, by using the satellite image data and forecasting the cloud imageries, it was possible to grasp the characteristics of the insolation, prevent erroneous prediction, and improve the prediction accuracy.
Development of a Low Cost Crow Deterrent AI System

Damage caused by crows such as damage to crops, scattering garbage on the streets, assaulting people walking beneath their nests, etc. has become a problem in various parts of the country. There are several damage abatement techniques such as protective nets, scarecrows, flashing lights, etc. However, existing methods have limited effects and are not always practical. Research using promising AI systems use expensive equipment. In this research, we propose an end-to-end real-time crow deterrent system based on the latest deep learning technology, using low-cost equipment. The proposed system detects the crow arrival and generates randomly frightening noises, including predator or bird distress calls. We conducted various experiments and confirmed the effectiveness of the proposed system.
Aggregate Signature Schemes with Traceability of Devices Dynamically Generating Invalid Signatures

Aggregate signature schemes enable us to aggregate multiple signatures into a single short signature. One of its typical applications is sensor networks, where a large number of users and devices measure their environments, create signatures to ensure the integrity of the measurements, and transmit their signed data. However, if an invalid signature is mixed into aggregation, the aggregate signature becomes invalid, thus if an aggregate signature is invalid, it is necessary to identify the invalid signature. Furthermore, we need to deal with a situation where an invalid sensor generates invalid signatures probabilistically. In this paper, we introduce a model of aggregate signature schemes with interactive tracing functionality that captures such a situation, and define its functional and security requirements and propose aggregate signature schemes that can identify all rogue sensors. More concretely, based on the idea of Dynamic Traitor Tracing, we can trace rogue sensors dynamically and incrementally, and eventually identify all rogue sensors of generating invalid signatures even if the rogue sensors adaptively collude. In addition, the efficiency of our proposed method is also sufficiently practical.
Data Covert Channels between the Secure World and the Normal World in the ARM TrustZone Architecture

The ARM TrustZone architecture, which provides hardware-assisted isolation, is widely adopted in mobile and IoT devices. The security of ARM TrustZone relies on the idea of splitting system-on-chip hardware and software into two worlds, namely normal world and secure world. There are legitimate channels at the hardware level that the normal world and the secure world can use to communicate with each other. To protect these channels from being abused, research efforts were invested on restricting the access to these channels from normal world components. Therefore, only predefined and legitimate normal world components can use cross-world communication channels. In this work, we present a study on data covert channels that can bypass such protection mechanisms and smuggle sensitive information. We first analyze causes of the noise in the covert channel between two worlds. Then, we evaluate the accuracy and bandwidth of covert channels built by our PRIME+COUNT method with one built by PRIME+PROBE method. Our results demonstrate that PRIME+COUNT is an effective technique for enabling cross-world covert channels in the ARM TrustZone.