Reviewerly/exHarmony huggingface dataset:
A Wrapper based feature extraction framework based on <scp>AlexNet</scp> deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Abstract In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.
Cryptoanalysis on the optical image encryption scheme based on full phase encoding and equal modulus decomposition

In this paper, the security of a security-enhanced optical cryptosystem based on full phase encoding and equal modulus decomposition (EMD) is evaluated. Compared to the original EMD-based image scheme in which plaintext is the amplitude information of the spectrum to generate two complex-valued masks with equal moduli, phase-encoded plaintext is regarded as the input of EMD-based structure to generate masks in the full phase encoding and EMD combined cryptosystem. It seems that the security strength has been improved by decreasing the number of constraints in the iterative attack; however, it is found that this scheme is still under security risk. Thus, we propose two iterative attacks based on normalization operator and phase-retrieval techniques with different constraints to break the security-enhanced scheme. Numerical simulations are carried out to demonstrate the feasibility and effectiveness of the proposed attacks.
SUCCESS STORY IN ADAPTING MASTER'S TEACHING TO COVID-19

In March 2020, the students of the Master's Degree in Financial Advice and Planning (MAPF) at the Rey Juan Carlos University suffered the uncertainty of how the health security restrictions that had to be adopted due to the irruption would affect their academic performance of COVID-19 in Spain. In this study, the evolution of the time series of the success rates of all subjects of this master's degree has been analyzed, taking a study period that begins in normal times and ends after these last two courses affected by COVID-19. The results show that the academic performance of the students in the last two years has not been worse at all, even observing an improvement in the academic performance of the students in the online mode. This can only be interpreted as a success for the university, teachers and students in the face of the challenge posed by the emergence of COVID-19 and the adaptation of teaching. Resumen En marzo de 2020 los alumnos del Máster Universitario en Asesoramiento y Planificación Financiera (MAPF) de la Universidad Rey Juan Carlos sufrían la incertidumbre de cómo les iba afectar en su desempeño académico las restricciones por seguridad sanitaria que se tenían que adoptar a causa de la irrupción del COVID-19 en España. En este estudio se ha analizado la evolución de las series temporales de las tasas de éxito de todas las asignaturas impartidas en este máster, tomando un periodo de estudio que se inicia en tiempos de normalidad y que finaliza tras estos dos últimos cursos afectados por el COVID-19. Los resultados muestran que el desempeño académico de los alumnos estos dos últimos cursos no ha sido en absoluto peor, observándose incluso una mejora en el rendimiento académico de los alumnos de la modalidad online. Esto solo puede ser interpretado como un éxito de la universidad, profesorado y alumnos ante el reto que supuso la irrupción del COVID-19 y la adaptación de la docencia.
Next-Generation Payment System for Device-to-Device Content and Processing Sharing

Recent developments in telecommunication world have allowed customers to share the storage and processing capabilities of their devices by providing services through fast and reliable connections. This evolution, however, requires building an incentive system to encourage information exchange in future telecommunication networks. In this study, we propose a mechanism to share bandwidth and processing resources among subscribers using smart contracts and a blockchain-based incentive mechanism, which is used to encourage subscribers to share their resources. We demonstrate the applicability of the proposed method through two use cases: (i) exchanging multimedia data and (ii) CPU sharing. We propose a universal user-to-user and user-to-operator payment system, named TelCash, which provides a solution for current roaming problems and establishes trust in X2X communications. TelCash has a great potential in solving the charges of roaming and reputation management (reliance) problems in telecommunications sector. We also show, by using a simulation study, that encouraging D2D communication leads to a significant increase in content quality, and there is a threshold after which downloading from base station is dramatically cut down and can be kept as low as 10%.
A Novel Stereo Matching Pipeline with Robustness and Unfixed Disparity Search Range

Stereo matching is an essential basis for various applications, but most stereo matching methods have poor generalization performance and require a fixed disparity search range. Moreover, current stereo matching methods focus on the scenes that only have positive disparities, but ignore the scenes that contain both positive and negative disparities, such as 3D movies. In this paper, we present a new stereo matching pipeline that first computes semi-dense disparity maps based on binocular disparity, and then completes the rest depending on monocular cues. The new stereo matching pipeline have the following advantages: It 1) has better generalization performance than most of the current stereo matching methods; 2) relaxes the limitation of a fixed disparity search range; 3) can handle the scenes that involve both positive and negative disparities, which has more potential applications, such as view synthesis in 3D multimedia and VR/AR. Experimental results demonstrate the effectiveness of our new stereo matching pipeline.
Adaptive Bright and Dark Channel Combined with Defogging Algorithm Based on Depth of Field

Aiming at the problem that the dark channel prior algorithm is not ideal in removing fog at the distant view and the sudden change of depth, this paper proposes an adaptive bright and dark channel combined with defogging algorithm based on the depth of field. The algorithm divides the image into a distant area and a near area based on the depth of field. The near area uses the dark channel to remove the fog, and the distant area uses the bright channel to remove the fog, combined with the use of adaptive superpixel segmentation to give the content-based local area division and defogging control factor based on depth of field. It better solves the problem of large transmittance deviation and defogging distortion under content changes and sudden depth of field changes. From the subjective and objective indicators, the algorithm in this paper has achieved a good defogging effect, and the main indicators and comprehensive defogging quality are better than common algorithms.
Sample complexity of rank regression using pairwise comparisons

We consider a rank regression setting, in which a dataset of N samples with features in Rd is ranked by an oracle via M pairwise comparisons. Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher with respect to the underlying total ordering. A learner observes a dataset of such comparisons and wishes to regress sample ranks from their features. We show that to learn the model parameters with ϵ>0 accuracy, it suffices to conduct M∈Ω(dNlog3N/ϵ2) comparisons uniformly at random when N is Ω(d/ϵ2).
Smart Solar Tracker With Energy Monitoring

Renewable energy is quickly gaining importance as an alternative energy resource since fossil fuels are limited and their prices are very costly, sun being the biggest source of free energy. The main aim is to utilize the energy getting from the sun in the most efficient way. Also, farmers and other non-technical people in our country are unable to calculate the power consumed and amount of back-up that will be getting according to the load connected to the battery. Thus, the proposed system gives the solution for both the problems by making proper and efficient use of it to solve the crisis of reduction in fossil fuels, since solar is available in abundance. This is a smart system which aims to develop a dual axis solar tracker with an IoT (Internet of ThingS) monitoring system using a microcontroller. Solar panels must be aligned with the sun using a system that tracks the sun in order to optimum power output. Using panels that can revolve along an axis in relation to the location of the sun can increase conversion efficiency by at least 30-40%. Proposed system can be remotely operated using IoT .This report represents the design of a smart solar tracking system which is based on the MSP430 Microcontroller which provides movement of the solar panel in dual axis mode in direction where maximum sunlight is incident. The data which is collected from the system is stored in a cloud. So as it is observed, a two-axis solar tracking system generates more power. It is easier to maintain, no electricity required, no fuel cost and easy to install with long operating life.
Visualization Research of College Students’ Career Planning Paths Integrating Deep Learning and Big Data

As China’s education enters a high-level stage, more and more students graduate from Chinese colleges and universities. In particular, the current employment environment is flexible and multilateral, and there are more and more opportunities to choose from. In view of this situation, this article aims to visualize the career planning (CP) path of college students, so as to help college students adapt to the environment of flexible employment. For deep learning and big data (DLBA) technology, this article proposes the LSTM-Canopy algorithm, which is added to the traditional Canopy algorithm to enhance the self-learning clustering ability of the algorithm. Also, this study applies this algorithm to the visualization system of college students’ CP path, which can effectively improve the analysis and judgment of experts on career. The experiments in this article have proved that the system can meet the normal use of 400–500 users, and the system server has successfully passed 40 load tests, and the running time is also less than 2.5s, which proves the reliability of the system.
Portraits Editing using StyleGANs &amp; Transfer Learning

The untangled latent space and the high-quality production style Generative Adversarial Networks has enabled a variety of image altering applications. For diverse tasks, new structures or task-specific training paradigms are frequently necessary. In this study, the spatial characteristics of Style GAN is investigated. It is proven that, without any additional architecture, tasks like image mixing, panorama creation, generation from a single picture, controlled and local multi - modal image to picture translation, and attribute transfer can be performed utilizing a pre-trained Style GAN that are equivalent to state-of-the-art approaches. There are currently a variety of models, primarily Generative Adversarial Network (GAN) for various image manipulation works. One such kind of GAN is Style GAN, because of which high quality image editing is possible. However not all tasks can be achieved using the style GAN method and to carry out those tasks some contiguous properties of style GAN have to be worked on. And with, a list of functions, such as blending of pictures, panorama creation as well as generation from a single image, controlled and local multimode image to image conversion, can be performed comparable to state-of-the-art algorithms. The proposed method is straightforward, effective, and efficient, and it may be used with any Style GAN model that has already been trained.
Building Unmanned Store Identification Systems Using YOLOv4 and Siamese Network

Labor is the most expensive in retail stores. In order to increase the profit of retail stores, unmanned stores could be a solution for reducing labor cost. Deep learning is a good way for recognition, classification, and so on; in particular, it has high accuracy and can be implemented in real time. Based on deep learning, in this paper, we use multiple deep learning models to solve the problems often encountered in unmanned stores. Instead of using multiple different sensors, only five cameras are used as sensors to build a high-accuracy, low-cost unmanned store; for the full use of space, we then propose a method for calculating stacked goods, so that the space can be effectively used. For checkout, without a checking counter, we use a Siamese network combined with the deep learning model to directly identify products instantly purchased. As for protecting the store from theft, a new architecture was proposed, which can detect possible theft from any angle of the store and prevent unnecessary financial losses in unmanned stores. As all the customers’ buying records are identified and recorded in the server, it can be used to identify the popularity of the product. In particular, it can reduce the stock of unpopular products and reduce inventory.
Improving Medical Q&amp;A Matching by Augmenting Dual-Channel Attention with Global Similarity

The emergence of online medical question-answer communities has helped to balance the supply of medical resources. However, the dramatic increase in the number of patients consulting online resources has resulted in a large number of repetitive medical questions, significantly reducing the efficiency of doctors in answering these questions. To improve the efficiency of online consultations, a large number of deep learning methods have been used for medical question-answer matching tasks. Medical question-answer matching involves identifying the best answer to a given question from a set of candidate answers. Previous studies have focused on representation-based and interaction-based question-answer pairs, with little attention paid to the effect of noise words on matching. Moreover, only local-level information was used for similarity modeling, ignoring the importance of global-level information. In this paper, we propose a dual-channel attention with global similarity (DCAG) framework to address the above issues in question-answer matching. The introduction of a self-attention mechanism assigns a different weight to each word in questions and answers, reducing the noise of "useless words" in sentences. After the text representations were obtained through the dual-channel attention model, a gating mechanism was introduced for global similarity modeling. The experimental results on the cMedQA v1.0 dataset show that our framework significantly outperformed existing state-of-the-art models, especially those using pretrained BERT models for word embedding, improving the top-1 accuracy to 75.6%.
A time for benevolence. Trust between students and faculty at Chilean public high schools during the COVID-19 crisis

The COVID crisis has caused that the way of teaching classes in schools must be reinvented, but it has also transformed other dimensions of relationship between teachers and students. This article collects qualitative information regarding how high school teachers have developed actions to interact with their students, as well as the vision that the latter have regarding whether said actions increased their trust in them. The information was collected through semi-structured interviews conducted online with 36 teachers and 36 students in 6 selected public high schools in Santiago Chile. The analysis distinguished the actions developed by teachers to support students regarding material deprivation of their families or their emotional affectation, and others that promote remote academic activities. The results show that, along with restricting interactions destined for classes, teachers opened or intensified virtual opportunities for individualized relationships around dimensions of the students' lives that were usually left out of face-to-face interaction. The actions that most managed to advance student trust in their teachers were linked to benevolence, and their concern for their personal situation or that of their classmates. It is more doubtful, however, that greater trust has developed in terms of the competence of teachers to promote academic learning.
Underwater color restoration and dehazing based on deep neural network

Abstract In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an underwater unsupervised generative adversarial network call UWGAN for generating realistic underwater fake images from paired images in air and their corresponding depth maps based on an improved underwater imaging model. Secondly, U-Net, which is trained efficiently using synthetic underwater dataset, is adopted for color restoration and dehazing. Our model directly reconstructs underwater clear images using end-to-end autoencoder networks, while maintaining the structural similarity of underwater scenes. The results obtained by our method were compared with existing methods qualitatively and quantitatively. Experimental results obtained by the proposed model demonstrate well performance on open real-world underwater datasets, and the processing speed can reach up to 125FPS running on one NVIDIA 1060 GPU.
In Between Centre and Periphery: Kenya as a Key Scientific Nation in East Africa?

Kenya’s government has identified science, technology and innovation as key for its national development plan and has started to refurbish its research environment. In this article, we use the world system approach to discuss the largely peripheral relations of Kenya’s science systems to the global science system and to identify indications for Kenya becoming a semi-peripheral scientific player itself within Eastern Africa. While the publications are dominantly oriented towards the Global North and while foreign sources fund nearly half of Kenya’s research and development (R&amp;D), the country starts to become an important country for its neighbours. However, Kenya is still facing an unstable system of integrating significantly more graduate students. These are seen as essential to provide for a sustainable knowledge base that is required for the country’s socio-economic goals. We point to the lack of robust and recent data on R&amp;D in Kenya as an impediment to evidence-based policy-making.
The use of clay modeling to increase high school biology vocabulary learning

Purpose The paper explored the benefits as well as the concerns of vocabulary learning with clay modeling in terms of practical and pedagogical implications for creating positive learning experiences. Design/methodology/approach A mixed-methods design was conducted to examine the effectiveness of vocabulary learning with clay modeling practices in lower socioeconomic status schools. Findings Although test results showed no statistically significant differences between the groups, the clay modeling group did improve vocabulary acquisition similar to the sentence writing group. The students were actively engaged with hands-on activities using the clay and also demonstrated positive emotional, behavioral and physical experiences. Research limitations/implications The addition of the clay modeling provided an opportunity for kinesthetic learning but created a high extraneous cognitive load with the challenges incurred through the use of clay. Practical implications The challenges can be reduced by 1) adopting appropriate instructional strategies to design and implement effective clay modeling activities for students and teachers, 2) providing training or professional workshop development for teachers and 3) ongoing practical support and assistance for students. Social implications Exploring the use of kinesthetic instructional practice at the high school level may prove beneficial since clay modeling is frequently used effectively at lower grade levels. Originality/value The current study explores the added value of clay modeling for high school students’ biology vocabulary learning in a lower socioeconomic status school from practical and pedagogical perspectives.
Pattern-of-Life Activity Recognition In Seismic Data

Pattern-of-life analysis models the observable activities associated with a particular entity or location over time. Automatically finding and separating these activities from noise and other background activity presents a technical challenge for a variety of data types and sources. This paper investigates a framework for finding and separating a variety of vehicle activities recorded using seismic sensors situated around a construction site. Our approach breaks the seismic waveform into segments, preprocesses them, and extracts features from each. We then apply feature scaling and dimensionality reduction algorithms before clustering and visualizing the data. Results suggest that the approach effectively separates the use of certain vehicle types and reveals interesting distributions in the data. Our reliance on unsupervised machine learning algorithms suggests that the approach can generalize to other data sources and monitoring contexts. We conclude by discussing limitations and future work.
Collaboration or separation maximizing the partnership between a “Gray hat” hacker and an organization in a two-stage cybersecurity game

Vulnerability disclosure is a key topic in cybersecurity. It is a practice ensuring that organizations address and fix vulnerabilities before bad actors can find and exploit them. This study focuses on the “disclose or exploit” dilemma. It presents a two-player non-zero-sum simultaneous cyber-security game between a hacker and an organization at multiple rounds. The vulnerabilities classified as high, medium, and low are based on a Common Vulnerability Scoring System (CVSS). The hacker can decide to act separately or to collaborate with the organization. Subsequently, the organization chooses to operate individually or cooperate with the hacker. The organization also has a budget limit to patch the vulnerabilities. The paper developed an algorithm to determine the Nash equilibria of the game and conducted a numerical analysis. It found that maximum cooperation occurred at the beginning of the game when both the organization and the hacker decided to cooperate.