Reviewerly/exHarmony huggingface dataset:
An Incentive Mechanism for Reporting Phishing E-Mails Based on the Tripartite Evolutionary Game Model

The human is considered as the important link in the phishing attack, and the e-mail security provider encourages users to report suspicious e-mails. However, evidence suggests that reporting is scarce. Therefore, we study how to motivate users to report phishing e-mails in this paper. To solve the problem, a tripartite evolutionary game model among e-mail security providers, e-mail users, and attackers is constructed. We obtain the desired evolutionary stable strategy through solving the replicator dynamics equations. Moreover, the evolution process to the desired evolutionary stable strategy is derived, which can guide the e-mail security provider to make a reasonable incentive mechanism. Lastly, we experiment with a large real-world e-mail network. The experiment results show that our model is effective and practical.
Rack Scale Area Detection Based on the Improved Efficient and Accuracy Scene Text

In the collection of equipment information in the machine room, the location information of the equipment on the rack is often taken by manually observing the scale and estimating it, which is inefficient and highly error-prone. To reduce the misrecognition rate, this paper uses a deep learning method to detect the rack-scale area and provide management personnel with guidance for reading. The current network that can better complete the task of scene character detection has the EAST (Efficient and Accuracy Scene Text). The rack-scale area in this application scenario belongs to small targets, so this network is difficult to meet the detection task of small targets. In this paper, the structure of the EAST is adjusted to address the problem. The ResNet_50 is used to replace the PVANet as the feature extraction network, which expands the number of feature map's channels and improves the detection ability of small targets. Meanwhile, BLSTM was added to the original network to improve the network's ability to process serialized data. The F-Score value obtained in the experimental test is 85.33%, which meets the task requirements well.
The Whole Versus the Parts: The Challenge of Compositional Data Analysis (CoDA) Methods for Geochemistry

In complex geochemical aqueous systems, chemical species are conceptually distinct but empirically related thanks to a large number of interactions taking place at different spatial and/or temporal scales. In this condition, common elements are shared, multiplicative interactions arise, and feedback mechanisms may be able to maintain the system far from the thermodynamical equilibrium, bearing wide fluctuations. Chemical species can have alternative stable states and transitions among them that could produce important consequences for the stability and the resilience of the solutions, also forced by climate changes and with impact on human health. Under the Compositional Data Analysis (CoDA) methodology, it is possible to appreciate the power of some tools able to take a look at the whole instead of the constituting parts, enhancing the understanding of the nature of mutual interactions. In this research work, the role of the perturbation operator governing addition/subtraction in the simplex geometry is explored as a way to trace compositional changes and investigate the system dynamics. The results of our approach on the chemistry of the Arno River waters (Central Italy) highlight the possibility to discover the resilience of chemical species under the pressure of the environmental drivers affecting the catchment. Geochemical mobility (e.g., ionic potential, ionic strength) can be associated with new tools that provide information on either the resistance to change, predisposing the system to critical shifts, or its adaptive capacity, which instead favors gradual changes. This information appears to be fundamental since river water chemistry enables to decipher processes at the boundaries among lithosphere, biosphere, hydrosphere, and atmosphere, all key reservoirs involved in the dynamics of the Earth. This knowledge will be particularly relevant if the pressure of the climatic changes on our planet will continue to increase.
A Hybrid LWT and DCT-Based Lossless Watermarking Scheme for Color Images

This paper introduces a hybrid lossless color image watermarking scheme using lifting wavelet transform (LWT) in discrete cosine transform (DCT) domain. A scrambled watermark image corresponds to Arnold transform is embedded. The two-level LWT is performed on a host image and LL subband is chosen, followed by DCT for embedding the scrambled watermark. The performance of the proposed scheme has been validated in terms of PSNR and NC. The various image processing attacks are encountered to investigate the robustness.Singh, Roop Ashok, Alaknanda Saraswat, Mukesh
Analysis on the Internal Logic and Path of First-class Discipline and First-class Specialty Construction under the Background of Double First-class

The main objectives and tasks of the "double-first-class" construction in universities is First-class discipline construction and first-class specialty construction. The knowledge transmission from discipline construction to specialty construction and the talent transmission from specialty construction to discipline construction in turn have interactive and long-term relationships. However, there are also many differences and conflicts, such as system conflicts caused by differences in evaluation systems, investment conflicts caused by differences in resource allocation, and cultural conflicts caused by differences in teacher identification. According to this, this paper proposes the classified development of colleges and universities. Different types of colleges and universities should have different ideas for discipline and specialty construction, especially for research-oriented universities and teaching-oriented universities. For research-oriented universities, specialty construction should be based on discipline, while for teaching-oriented universities, discipline construction should be based on specialty. Therefore, in the process of Double-First-class construction, colleges and universities should grasp the relationship between disciplines and specialties, find out their own position, and choose appropriate construction ideas and development paths.
Incidental detection of malaria parasite in automated hematology 6-Differential analyzer

India has been facing the problem of malaria for centuries. Peripheral smear microscopy remains the gold standard for diagnosis; however, it is a tedious process and requires qualified staff. Flow cytometric-based hematology analyzers' scattergrams can be of vital use in identifying the abnormal scattergrams for malaria. We report a patient with fever and chills who presented to the outpatient department and was further evaluated for pyrexia of unknown origin. 6-Diff complete blood count analyzer incidentally showed abnormal scattergram which was evaluated by a pathologist and confirmed the presence of Plasmodium vivax. The accuracy in detection of malaria diagnosis can vary based on species, parasite load, immunity, and clinical context. Pathologist and technical staff should analyze any abnormal scattergram and hematological data along with peripheral smear to identify the parasites.
The Mobile Phone, Information Sharing and Financial Sector Development in Africa: A Quantile Regressions Approach

This study investigates linkages between the mobile phone, information sharing offices (ISO) and financial sector development in 53 African countries for the period 2004-2011. ISO are private credit bureaus and public credit registries. The empirical evidence is based on contemporary and non-contemporary quantile regressions. Two main hypotheses are tested: mobile phones complement ISO to enhance the formal financial sector (Hypothesis 1) and mobile phones complement ISO to reduce the informal financial sector (Hypothesis 2). The hypotheses are largely confirmed. This research adds to the existing body of literature by engaging hitherto unexplored dimensions of financial sector development and investigating the role of mobile phones in information sharing for financial sector development.
Transformer Based Multi-Agent Framework

We present a Transformer-like agent to learn the policy of multi-agent cooperation tasks, which is a breakthrough for traditional RNN-based multi-agent models that need to be retrained for each task. Our model can handle various input and output with strong transferability and can parallel tackle different tasks. Besides, We are the first to successfully utilize transformer into a recurrent architecture, providing insight on stabilizing transformers in recurrent RL tasks.
Development of Tangible Interfaces for Cognitive Development

This chapter presents the development of a low-cost tangible interface to enhance children's learning through collaborative storytelling, using a set of movable blocks, with a high degree of autonomy and the ability to exchange information with each other. Taking into account the context of the existing game model and on the basis of which this study was developed, the function of these removable blocks is to allow children to create and recreate their own narratives in permanent interaction by manipulating the physical blocks, which trigger the associated virtual representations (characters, e.g., animals, objects, locations, or others). It is expected that children will be able to project these representations on a screen through the activation and manipulation of the physical blocks by adding, removing, moving the blocks while interacting with each other and creating narratives. The authors have attempted to apply a method that could present a new approach to the educational gaming industry.
Detection of Anomaly in Streaming Dataset

Totad, Shashikumar G. Karibasappa, K. G. Geeta, R. B. Nithish, T. Hiremath, Prasanna J. Elagoudanavar, Rahul Rao, NishaData is composed of actual data that translates to a sequence that can be processed by the machine. Data can be analysed and used to enhance the revenue of the company, to create a new chemical formulation, to make unique applications and to aid in the establishment of innovative modes of communication. The key element of work for any industry is to analyse the data acquired and adapt trends or achieve economic insights to tackle any given problem. The analysis of data is as qualitative as the accuracy of the information. The data obtained should be accurate and consistent. Improper data collection may lead to incorrect data visualizations resulting to inaccurate analysis and erroneous results. Most industries aim for accuracy and better performance where a tiny mistake may lead to heavy losses. In this condition, the incorrect decisions taken due to improper data analysis may lead to crucial circumstances. In this work, we propose a hybrid model which is the combination of long short-term memory (LSTM) and recurrent neural network (RNN) for detecting the anomalies or abnormalities in the data during the request raised by the customer. The proposed model is found to be even more efficient in identifying an anomaly in the data collection, taking into consideration of date and time constraints. The proposed model delivered enhanced performance compared to other traditional approaches.
The Analysis of Top Cyber Investigation Trends

Cybersecurity is an ever-evolving area of technology. As such, there will always be myriad trends to consider. Through the progression of cybersecurity comes the increased need for organizations to keep pace with the rapid development of technology. However, the current skills gap of cybersecurity professionals has overwhelmingly become a cause for concern. The spread of cloud computing has created a need for new cloud forensics procedures, and the use of internet-connected medical devices has added concerns for the information security structure of many organizations. In order to resolve these issues, proper vulnerability testing and implementation of new processes to keep up with the changes in technology have to be introduced to reduce the possibility of hacking incidents and aid in remediation. If more organizations leverage the skills and personnel available to them, there are ways to reduce the skills gap and other issues affecting cybersecurity.
Research on the Operation Mode of Infrastructure Projects Based on “REITs+PPP”

Abstract Based on the complex international situation and the continued impact of the epidemic, infrastructure investment has become an important means to promote domestic economic recovery, combined with infrastructure public offering REITs policy documents, constructing an operation mode combining “REITs+PPP”, analyzing the operation process and applicable project scope of the operation model, Finally, the main problems existing in the model are explained and corresponding solutions are proposed.
Experience of edition of training literature for railwayers

Discusses the experience of publishing educational literature for students of educational organizations under the jurisdiction of the Federal Agency for railway transport.
Information technologies as a means of patriotic education of junior pupils

The study examines the theoretical aspects of the patriotic education of primary schoolchildren in the context of the use of information technologies, defines the meaning and functions of information technologies in the educational process of school education. The patriotic upbringing of primary school students is more than relevant, since the preservation and revival of cultural heritage begins with a small homeland (family, courtyard, school, etc.) and is a key moment in the upbringing of the modern generation. In order to study the level of patriotic education of primary school students, we conducted a survey of schoolchildren, the results of which showed: most of them have a low level of patriotic education, which is due to the lack of due attention of the state as a whole and educational institutions in particular to the issue of patriotic education of schoolchildren. Thus, the problem of patriotic education of primary schoolchildren in the context of digitalization of education has acquired new relevance in connection with new views on the quality of upbringing and education of the younger generation.
Impacts of Kernel Factorization on Different Resized Images in Object Recognition Base on Inception Convolutional Neural Network

This paper investigates impacts of kernel factorization on different resized images in object recognition based on three inception-block modules of inception convolutional neural networks (ICNNs), namely ICONV3 and ICONV131. The ICONV3 employs 3×3 kernel-size convolutions according to each inception-block; whereas the ICONV131 equivalently uses 1×3 and 3×1, replacing one 3×3 convolutions. The experiments rely on two different resized 50×50, 100×100 pixels regarding 60,000 samples of 32×32, in 10-class benchmark CIFAR-10 image dataset. Recognition performance evaluations depend on averages of precision, recall, F1 and accuracy scores, based on 10-fold cross validation for bias reduction purpose. The results indicates approximate 5% obviously better recognition accuracy of using larger resized of CIFAR-10 than small ones according to both ICONV3 and ICONV131 models. Only few 0.2% to 0.3% different recognition performance are exhibited between ICONV3 and ICONV131 for both large and small resized images. This may cause one to say ICONV131 may be preferred to ICONV3 due to about 3.6% and 6% greater parameters used in ICONV3 than ICONV131, respectively regarding 50×50 and 100×100 resized images.
Emerging Trends in Medical Science Using Biometric

Biometrics in health care and medicine is one of the revolutionary technological advancement that has been made to improve and cater the requirements of patients as well as doctors. Technologies have continued to progress, affecting the performance of globally efficient medical healthcare systems. Meanwhile, privacy and security of patient data for most users of the health information management system have recorded major concerns. This is not denying the fact that privacy is necessary in-patient care. The record of the patient which may be inadequately retained or misconstrued may lead to wrong prescriptions or death. In this paper, we present an overview and application of biometric in medical science is discussed in detail. The use of biometrics for identification has played a major role in protecting privacy and medical healthcare systems. This paper also justifies the classical methods, influential methods, and taxonomy predicated on the biometric attributes.
Experimental Comparison of PC-Trees and PQ-Trees

PQ-trees and PC-trees are data structures that represent sets of linear and circular orders, respectively, subject to constraints that specific subsets of elements have to be consecutive. While equivalent to each other, PC-trees are conceptually much simpler than PQ-trees; updating a PC-tree so that a set of elements becomes consecutive requires only a single operation, whereas PQ-trees use an update procedure that is described in terms of nine transformation templates that have to be recursively matched and applied. Despite these theoretical advantages, to date no practical PC-tree implementation is available. This might be due to the original description by Hsu and McConnell [ 14 ] in some places only sketching the details of the implementation. In this paper, we describe two alternative implementations of PC-trees. For the first one, we follow the approach by Hsu and McConnell, filling in the necessary details and also proposing improvements on the original algorithm. For the second one, we use a different technique for efficiently representing the tree using a Union-Find data structure. In an extensive experimental evaluation we compare our implementations to a variety of other implementations of PQ-trees that are available on the web as part of academic and other software libraries. Our results show that both PC-tree implementations beat their closest fully correct competitor, the PQ-tree implementation from the OGDF library [ 6 , 15 ], by a factor of 2 to 4, showing that PC-trees are not only conceptually simpler but also fast in practice. Moreover, we find the Union-Find-based implementation, while having a slightly worse asymptotic runtime, to be twice as fast as the one based on the description by Hsu and McConnell.
Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting

The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.
High-precision Sub-pixel Object Tracking Algorithm

Aiming at the problem that the positioning accuracy of the KCF tracking algorithm is difficult to reach the pixel level and cannot adapt to the target scale variation well, the correlation filter tracking algorithm based on the scale pyramid achieves higher tracking accuracy, but the tracking speed is greatly reduced. Introducing the logarithmic polar coordinate transformation of the image, an object tracking algorithm based on the logarithmic polar coordinate transformation is proposed. First, the target template is transformed into the logarithmic polar coordinate, and the scale variation of the target is converted into a displacement signal, then extract the HOG features before and after the target template transformation, and the filter model of displacement and scale is established. Finally, the displacement and scale factor of the object are tracked synchronously under the framework of correlation filtering, and the two are merged to obtain the target tracking frame. The experimental results show that: The average overlap precision of the algorithm in this paper is high, and the tracking effect is better (Experiment 1). The algorithm in this paper can track rigid objects stably and can adapt to scale variation accurately very well (Experiment 2). The overall accuracy and success rate are in the first place (Experiment 3). The algorithm in this paper can approximately achieve pixel-level positioning accuracy, and the tracking speed can reach twice the traditional algorithm (Experiment 4).
Foldables and 2-in-1s: Understanding and Supporting the Needs of Hybrid Device Users

Foldables and 2-in-1 devices provide users with new ways to adapt their device to their mobile context, allowing users to switch device configuration, screen size and shape, and input mechanism in seconds. This has strong implications for the design of interactive media applications that need to adapt to the user's changing context and device parameters. In this study, we explored how users opportunistically make use of these new device affordances in their everyday lives through interviews with 15 diverse hybrid device owners, identifying use cases and pain points. Users reported regularly moving through multiple configurations or input modes while using a single product, or completing a single task. Based on our learnings, we provide recommendations for practitioners designing for hybrid mobile devices.
R2GSync and edge views

Graph databases that are used in enterprises are primarily extracted from a main transactional store that is often an RDBMS. This data infrastructure set up raises the challenge of keeping the extracted graph in a graph database management system (GDBMS) in sync with the source RDBMS. When the extracted graphs contain edge types that are results of join queries, this synchronization requires incrementally maintaining these join queries. In this paper, we investigate an alternative design where we can map the individual relations in these joins to virtual nodes and edges to keep the synchronization very efficient and instead support view-based querying in the GDBMS. We present a system called R2GSync, that synchronizes an RDBMS with a GDBMS and our accompanying edge view design for a GDBMS. We describe our implementation of edge views in GraphflowDB and query optimization techniques for improving the performance of queries that involve edge views.
Computer aided mass segmentation in mammogram images using Grey wolf Optimized Region growing technique

One of the dangerous threats, that affect women all around the globe is breast cancer, leading to early mortality in women. According to researchers the survival rate of the breast cancer affected person can be improved by a greater amount by its early detection. Hence, there is need for development of an automated system, which can act as an aid for supporting the radiologists in making proper diagnostic decision. The proposed work involves detection of the breast masses by making use of an optimized region growing method, in which the optimal seed point selection and optimal threshold generation was achieved using Grey Wolf Optimization (GWO). In the proposed work the extraction of both global and local features are being considered. The global features considered includes shape features, Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) for extracting texture feature and local texture feature is extracted using Local Binary Pattern (LBP) and Scale invariant feature transform (SIFT). The fusion of the local and global features were being fed to Support Vector Machine (SVM) classifier, which differentiates the masses as either benign or malignant in nature. The proposed methodology achieved a highest accuracy of 96% by the fusion of global texture feature GLCM and LBP.