Reviewerly/exHarmony huggingface dataset:
The development of Android-based thin layer chromatography learning material with project-based learning

The topic of thin layer chromatography material is learned at the Vocational High School (VHS) level precisely in professional competency of analytical chemistry. The results of observation show that ICT-based thin layer chromatography teaching materials have not been developed. Alternatively, teaching materials are developed on an Android media and use a project based learning model (PjBL). This is in accordance with the characteristics of learning in VHS technical and applicative, so the PjBL model is suitable to be applied in VHS. In addition, related to conditions such as the Covid-19 pandemic and the high use of Android in Indonesia, Android-based teaching materials have the potential to be online teaching materials. The purpose of this research is: (1) to develop thin layer chromatography teaching materials with a project based learning model based on Android for professional competency of analytical chemistry in Vocational Schools, (2) know the level of its validation, and (3) to describe the potential of android-based teaching materials developed. The development research model used is the 4-D model, namely the define stage, the design stage, the develop stage, and the disseminate stage. However, the disseminate stage was not carried out due to time constraints and research costs. Data collection instruments are validation/questionnaire sheets. Validation was carried out by 2 chemistry lecturers and 2 chemistry teachers. Readability test was conducted by 25 students. Research data in the form of qualitative and quantitative data. The data analysis technique uses percentage calculation techniques and qualitative descriptive techniques. The results of the validation of teaching materials are 81.16% and the results of the validation of the lesson plan are 86.6% so that the teaching materials are categorized as very feasible. Readability trial results are 86.0% and classified as very feasible. Therefore, these results are potential as online teaching materials.
Blind multipurpose watermarking with insertion of a single watermark: a generic construction based on verifiable threshold secret sharing

Currently, the existing literature on multipurpose watermarking employs multiple watermarks for simultaneous achieving of multiple security objectives. This approach can be problematic since a watermark targeting tampers might fail to detect tampering of another watermark inserted for copyright violation. Moreover, most of the current schemes follow a non-blind approach where the original watermark is needed for authentication. A question that naturally arises is whether multipurpose watermarking can be realised with the insertion of a single watermark in a blind way or not. The goal of the authors study is to provide an affirmative answer to this important question. They show how a cryptographic primitive called verifiable threshold secret sharing can be used to come up with a generic construction for blind multipurpose watermarking which inserts a single watermark into the host image for simultaneous achieving of copyright protection, authentication, and tamper localisation. The generic property of the proposed scheme provides flexibility for choosing the embedding/extraction of the watermark based on the desired level of fidelity, robustness and capacity. Experimental results are provided to confirm the superiority of their proposed technique as compared to existing approaches.
Neural Cross-Domain Collaborative Filtering with Shared Entities

Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Independent use of either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model – NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF is based on a wide and deep framework and learns the representations jointly using both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.
Facial Expression Recognition from 3D Facial Landmarks Reconstructed from Images

Direct classification of normalized and flattened 3D facial landmarks reconstructed from 2D images is proposed in this paper for recognizing eight types of facial expressions depicting the emotions of- sadness, anger, contempt, disgust, fear, happiness, neutral and surprised. The first stage is the 3D projection of 2D facial landmarks. The pre-trained convolutional Face Alignment Network (FAN) proposed recently for 2D/3D face alignment is used for the purpose. The 3D cartesian coordinates are translated to the spherical coordinate system prior to the classification stage. A variety of classifiers are tried and tested for classifying the 68 facial landmarks, for both the coordinate systems. The benchmark CK+ video dataset is used for the experimentation; the last frame of each video that depicts the peak of each emotion is used as the input image. The experimental results indicate that direct classification of normalized and flattened 3D facial landmarks in the spherical coordinate system yields the highest accuracy for the support vector machine classifier with grid search for determining optimal parameters.
Performance and Economic Analysis of a Standalone Solar Aeration System

The objectives of this article are to predict the performance, energy loss, cost effective analysis of a proposed photovoltaic (PV) aeration system in a rural area of Bangladesh by using the PVSyst simulation tool. Study of energy generation and various input parameters were conducted to assess the performance ratio (PR) and efficiencies. The results showed that 14.37 MWh of energy generation was possible per year from the proposed standalone PV system. The outcome indicates that the system has a yearly PR and energy losses of 61.5% and -29.5%, respectively. The economic feasibility study showed that the electricity generated using the proposed PV system costs USD 0.128/kWh, which is considerably cheaper in comparison with traditional electricity price USD 0.143/kWh. About 270.3 metric tons of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> could mitigate by the proposed solar PV systems. Based on this findings, the planned PV aeration system is found feasible to install in the proposed study area. This paper represents the economic and environmental development by solar energy (green technology) that lead to sustainability.
Dual-Stage Domain Adaptive Mitosis Detection for Histopathology Images

Histopathology images for mitosis detection vary in appearance due to the non-standard method of preparing the tissues as well as differences in scanner hardware. This makes automatic machine learning based mitosis detection very challenging because of domain shift between the training and testing datasets. In this paper, we propose a method of addressing this domain shift problem by using a two-stage domain adaptive neural network. In the first stage, we use domain adaptive Mask R-CNN to generate masks for mitotic regions. Thus generated masks are used by a second domain adaptive convolutional neural network to perform finer mitosis detection. Our method achieved state-of-the-art performance on both ICPR 2012 and 2014 datasets. We demonstrate that using a domain agnostic approach achieves better generalization and mitosis cell localization for the trained models.
DNN based Currency Recognition System for Visually Impaired in Sinhala

Recently researches have been conducted in the domain of currency recognition. The task of recognizing the currency notes has become challenging due to the distortion of the notes over time. Currency recognition systems in Sinhala for visually impaired people are rarely developed. To address this problem a research has been done and a relevant application has been implemented comprising three modules as Speech Recognition module, Currency Recognition module and Text to Speech Module. The major challenge in all three modules is to achieve a better accuracy using deep learning concepts. TensorFlow platform and Keras library were used to build the speech recognition neural network model for Sinhala spoken words. Deep learning neural networks were utilized for the development of currency recognition module and text to speech module.
Application of Attendance Control and Learning Activities Using WhatsApp for Preschool Education

This work presents the uses of WhatsApp to send the activities for preschool during class no presential developed by pandemic, where there is a high variability of hours of send. The proposal uses an attendance control based in active methodologies for preschool to see if the variability of send activities to platform of WhatsApp presents a spontaneous change. The results shown that the attendance control of children have a direct effect over the send of the activities for professor. This mean that is important to implement attendance system until of start with remote classes in especial for preschool educations
Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

New generations of small advanced aircraft may soon transform how people and freight are transported.Thus, ensuring safe and reliable operation of these systems, in all plausible scenarios, is of utmost priority.In this paper, we address this problem by proposing a method to verify the safety of an autonomous aircraft controller following a navigational policy.The policy is encoded using discrete vector fields that are meant to drive the vehicle to a goal state and away from obstacles or restricted airspace.The solution presented in this paper provides the ability to verify the policy, catching unsafe scenarios which may be missed by random Monte Carlo methods or general reachability analysis.After illustrating the main theoretical principles, a possible practical implementation is described and compared with analysis based on Monte Carlo simulations.The comparison shows an insightful example where Monte Carlo simulations fail to detect several corner cases that are uncovered by the proposed method.In the conclusion of the paper, we provide insights about possible future research to implement the proposed solution obtaining higher accuracy and efficiency.
Countering Illicit Transfer of Funds Abroad Improving the System of Countering Money Laundering

Every country has to deal with the problem of managing and monitoring cross-border cash flows. The purpose of the study is to analyse tendencies in cross-border transactions, determine potential risks arising from the on-going introduction of digital technologies and innovative financial flows and propose measures to counter illicit transfer of funds abroad. The study focuses on net outward transfers of financial resources by the private sector and natural persons. The dynamics of funds being transferred from the Russian Federation in the last decade in absolute and relative terms are alarming for two reasons. First of all, the amount of money leaving Russia remains significant. When comparing the total net outward transfers made by the private sector to GDP values, it could be seen that they showed the highest numbers during difficult phases of the financial crisis. In 2008 and 2014, net outward transfers were estimated at 7.5% of Russia's GDP. Moreover, during the most favourable periods for the economy, their number increases, hereby approaching the decreasing number of net outward transfers by the private sector. This pattern proves the significance of such cash flows for the Russian economy. Secondly, when dealing with the problem of large amounts of funds being transferred from the country, it becomes crucial to identify not only their sources, but also their recipients and destination. In the study, tendencies in the transactions marked suspicious by primary financial monitoring bodies were described and analysed according to the imposed restrictive measures. Finally, it was decided that additional restrictive measures and improved schemes of countering illicit transfer of funds from Russia are needed now that the financial support to affected industries is allocated from the state budget, and more digital technologies are used in providing cash flows.
Construction and Analysis of Teaching Evaluation Model of Chinese International Education Based on Web

This paper analyzes the knowledge requirements in the information processing of Web structured Chinese International Education and teaching evaluation, introduces the influential Chinese semantic resources and ontology knowledge at present, presents the composition model and construction method of Chinese knowledge base for web structured information processing, and verifies the model in deep web research. The purpose of this research is to make the computer process the web structured information in specific domain more comprehensively and effectively, and it has certain reference value for the in-depth study of ontology.
Professional standards for training of teachers to ensure the quality of education in a city

The conducted research has allowed identifying the main contradictions between educational and professional standards in the field of training of teachers and determining the most vulnerable links in ensuring a systematic approach to the organization of the educational process. The conducted analysis has resulted in a scientific justification of the necessary changes and reconciliations in educational and professional standards to update them and improve the quality of training of specialists in the teaching sector. An earlier analysis of federal educational standards has shown that changes in the content of competencies were aimed at implementing education as a service rather than a pedagogical process. This makes it difficult for the teaching staff to educate the younger generation of teachers in the traditional paradigm of national education. However, the latest generation of Federal State Educational Standards (FSES) indicates the need to take into account the requirements of professional standards that correspond to the professional activities of graduates. Besides, clause 3.5 of the FSES 3++ specifies the right of an educational organization “not to include professional competencies determined independently if there are mandatory professional competencies, as well as if the recommended professional competencies are included in the bachelor’s program” (Order of the Ministry of Education and Science of the Russian Federation of December 4, 2015 No. 1426). There is an evident discrepancy of requirements of the FSES of the last generation in terms of the scope of professional competences and the need to take into account these requirements to ensure teacher’s actions, presented in the professional standards, aimed at performing official duties by the teacher that allows considering the problem of compliance with the requirements proposed in the FSES and in professional standards of teachers to be very relevant. The research purpose is to conduct a comparative analysis of the requirements stated in the FSES and professional standards of teachers in the national education system.
Drag coefficient identification of high-spinning projectile using cubature Kalman filter

In order to obtain the aerodynamic parameters of a high-spinning projectile, a new parameter identification method is proposed based on the Cubature Kalman Filter (CKF). First of all, the motion equation of the spinning stabilized projectile is established by the 4D trajectory model. Second, unknown parameters are added to the state vector to obtain the augmented state vector. Next, a new filter is designed for the identification of the unknown parameters based on the basic theory of the CKF. Finally, the simulation results of the CKF are compared with those of the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The results of comparison show that the CKF method can effectively identify the aerodynamic parameters and the identification error is less than 1.2%. The CKF method has greater accuracy than the EKF and the UKF.
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems

Deep neural networks (DNNs) significantly improved the accuracy of optical character recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerability of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the F ast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding

As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.
New Programmatic OTP Algorithm

Abstract The huge improvement in the field of data innovation and the web presentation to a surge of infringement to go around and take data, the earnest requirement for the development of information assurance advancements and information encryption procedures. A new Programmatic OTP Algorithm using an unsystematic key generation for ciphering plaintext presented in this paper. This method, based on create randomly a number (n) called add number, where the plaintext characters converted to a binary form and splits into equal parts according to n value with 2n ciphering keys generated. The keys will be distributed on these parts to get ciphered text. Accordingly, one of these keys its generation number will be set for all different n’s. The different n’s occurred Consecutively to the number of characters consist the plain message unlike the traditional OTP algorithm. This method characterized by a facility that the same generated key can produce different ciphering text using 2n probability. The proposed methodology has been proved as perfect ciphering method compared with OTP using statistical tests
Cybersecurity Regulation of Insurance Companies in the United States

While cybersecurity has been an important issue for all business sectors due to the rapid development of and reliance on technology and the increasing sophistication of unlawful actors, it is particularly significant for insurance companies because of the nature of the industry. The internet makes it possible to collect and store massive amounts of data, and this in turn requires the utmost confidence of consumers in the companies collecting this data. The growing concern for cyber risks has compelled insurance regulators to devise and implement frameworks and rules for insurance companies to follow. In the United States, insurance regulation is controlled by the states. Invariably, the enthusiasm and speed of responses have been mixed. New York has implemented the Cybersecurity Requirements for Financial Services Companies, while South Carolina, Ohio, Michigan, and Mississippi have passed laws based on the Insurance Data Security Model Law published by the National Association of Insurance Commissioners (NAIC), a non-governmental entity created and composed of insurance commissioners of each state and territory. The specific provisions within these regulations differ, which creates inconsistencies throughout the United States. As more states adopt cyberspace policies regulating the insurance industry, the divergence could worsen. This paper examines the NAIC Model Law and regulations in various states, as well as advocates for a uniform standard across the United States based on the New York regulations due to its robust nature.
CSNet: Cascade stereo matching network using multi‐information cost volume

The disparity map produced by matching a pair of rectified stereo images provides estimated depth information and accurate distance calculations for autonomous driving. For most stereo matching networks, cost volume plays a crucial role in the accuracy of disparity maps. To increase the accuracy of disparity maps, an improved cost volume method called multi-information cost volume (MICV) is proposed, with the fusion of concatenation volume and improved correlation volume, which is calculated both in inner product space and Euclidean space, to measure the correlation similarity between features in the left and right images. The inclusion of a squeeze and excitation (SE) module further improves MICV by adjusting the contribution of the correlation volume. To refine the disparity map and enhance the semantics of small objects, a cascade stereo network called CSNet is proposed, with a dilation feature fusion unit (DFFU) to calculate and integrate disparity maps from three different scale branches. The smaller scale branches are gradually integrated into the larger scale branches to implement the transmission of semantic information. The proposed method was evaluated using several established benchmarks including the Sceneow, KITTI2012 and KITTI2015 datasets. Experimental results demonstrate that authors' method produces more accurate disparity maps compared with existing state-of-the-art methods.
A Survey of FPGA Based on Graph Convolutional Neural Network Accelerator

In recent years, with rapid development of deep learning, neural networks have been explored thoroughly and regularly structured neural networks has been more powerful than ever. However, people are still suffering from trying to adapted conventional techniques to unstructured data structures. This paper introduces theoretical basis for graph convolutional networks, and the concept behind FPGA acceleration. Besides, this paper introduces different FPGA based approaches trying to accelerate the procedures of graph convolutional networks. The paper ends with a view into the future, proposing shortcomings of the current design approaches as well as challenges for future ones.