Reviewerly/exHarmony huggingface dataset:
Analysis of Cybercrime on Social Media Platforms and Its Challenges

Social media is a weapon that is capable of construction as well as destruction. The real power of the prevailing social media platforms becomes evident by witnessing the influence created by these platforms on a large scale. It plays a significant role in everyday life. The rising popularity due to its ability to make people attached with kith and kin have paved the way for the world to share photos, feelings, videos, which bears a high-security concern. However, most social media users do not know the underlying security level(s) of the respective account(s), including which features of these social media have to be considered if there is a risky situation. The police are making lots of efforts to curb cybercrime at its root and reduce the crime rate. This research aims to understand the characteristics of cybercrimes taking place in social media and identify what kind of efforts the police should take to control cybercrime. Python programming is used for the analysis, where pre-processing and then percentage analysis is performed to understand the data. It is seen that uneducated and poor people have more tendency to commit cybercrimes. Also, the ages between 20 and 25 committed more crimes. Hence, this would help the police to identify the type of people who would create more crimes. These results would help the police to narrow down their search on criminals for better surveillance. The police must focus on those with these factors while monitoring social media.
Dynamics of skew information correlations in two coupled qubit-systems under the high nonlinearity of a parametric amplifier: Intrinsic decoherence model

In this paper, we study the dynamics of skew information correlations, beyond its concurrence entanglement, between two coupled qubits interacting with containing amplifier cavity fields. An analytical description has been obtained for the intrinsic decoherence model when the cavity fields are initially in a superposition of generalized Barut–Girardello coherent states. The concurrence and skew information quantifiers present nonlocal correlations with different oscillatory behaviors, which can be controlled by the two-qubit coupling and the initial cavity nonclassicality. The sudden death–birth phenomenon is observed only in the concurrence entanglement behavior. With the intrinsic decoherence, the stationary correlated two-qubit states can be generated, which have different potential applications in quantum information. It is found that the two-qubit coupling affects the generated nonlocal correlations (NLC) as additional decoherence, reduces the generated nonlocal correlations, and accelerates the stable correlations. The initial even-coherent nonclassicality enhances the nonlocal correlations, which are more robust against the intrinsic decoherence.
An Innovative Scheme of Hybrid Software Technology Teaching Mode Based on Micro-course

Abstract Blended teaching is a teaching mode that combines online digital online learning with offline face-to-face classroom teaching, and combines the advantages of online learning with the advantages of traditional teaching. Only by combining the two, so that they can complement each other, can we get the best learning effect. Teachers-led intense discussion among students is the key to the successful implementation of blended teaching. Blended teaching reform can greatly stimulate students’ interest in learning and improve teaching quality. Mixed teaching can not only fully reflect the students as the main body of the learning process, give full play to their autonomy, enthusiasm and creativity, but also embody the teacher as the leading role in the teaching process, playing its role of guidance, inspiration and control. In this paper, combined with the characteristics of College English teaching, through the analysis of micro class based blended teaching and College English teaching methods, to research and create the most suitable for the current college English blended teaching mode.
Application of Association Rule Mining in a Clothing Retail Store

In this paper, an attempt has been made to understand the buying pattern of customer with the help of market basket analysis, which is an important tool in modern retailing industry. Retailing is defined as the timely delivery of goods and services demanded by end customers at prices that are competitive and affordable. Through association rule in data mining, we have tried to understand consumer behavior, brand importance, seasonality effect, buying pattern, product basket from the data. Data mining is the practice of analyzing database to gather and generate new information. Association rule mining is a process of finding rules that govern relation between sets of items. Market basket analysis is a modeling technique based upon the theory that explains the buying relation between certain groups of items. The transactional data is collected from the retail clothing store “Try Us” located in Indore, Madhya Pradesh, from the period November 26, 2017, to September 19, 2018, with the help of Point of Sale (POS) and bar code scanner. “Try Us” is a small retail store with multiple brands and is planning to upgrade store to a multilevel store. Data mining would benefit the overall store performance. Frontline Solver® Analytic Solver Data Mining (XLMiner) is used for simulations.
Pairing-Based Cryptography

Following the patterns of the modern world, it is justifiable to say that Data is one of the most valuable assets today. This change in perspective has resulted in a usefulness and popularity boost to previously neglected fields like Information security and cryptography. Cryptography, i.e. the protection of Data and messages by converting them into a senseless/unreadable format, is an age-old concept. From the Roman times where it was used for conveying covert battle plans between generals in the army, to a much later time, when it was used for sending secret messages in wars between nations, to now, when it is used to protect every strand of data in a variety of uses from social messaging and networking sites to bank accounts for the privacy of users and national secrets. Over the years, cryptography has been modified countless times and yet, each form it has taken has had the sole purpose of being nearly impossible to crack, i.e. decrypt without knowing the secret keys. Out of the many methods/algorithms used for Encryption, each one has unique implementations, strengths and weaknesses. Pairing-based cryptography is one of the best methods known to us. It takes advantage of the Diffie–Hellman approach to make cracking the code difficult, and at the same time, it keeps computation fast. It is based on the pairing of elements from two cryptographic groups (a set based on/enveloping a binary operation which connects every two elements of the group to a third). The Diffie–Hellman Key Exchange works on the assumption that there are no secure channels, i.e. third parties (Hackers for instance) have access to every encrypted message being communicated. There are many procedures used for making groups and rings involved in the generation of our cryptographic groups like the (modified) Weil pairing, the Tate-Lichtenbaum Pairing, Eta pairing and Ate pairing. The directions provided by the method implemented result in different sub-problems and advantages which result in different security levels of our encryption technique. The combination of these pros, cons and uniqueness acts as different methodologies for the implementation of pairing-based cryptography. Although modifications to algorithms and inventions to new approaches keep being explored every day, the backbone of a vast majority of these implementations, however, has the same concept. This book chapter gives an introduction to pairing-based cryptography, the associated mathematical concepts, definitions and procedures and associated algorithms used for implementation. Since the main motive behind cryptography is to aid in the field of Information Security, the fulcrum of issues faced/areas of judgement for all encryption techniques to be implemented is the un-crackability/strength of the algorithm used; the reverse-engineering methods for these algorithms will also be discussed. Furthermore, there are many implementation techniques being discovered everyday which when combined with existing algorithms have scope for improvement in the future. Some of which are also mentioned.
Counting a pot of gold: A till golden standard (AuGC-1)

Determining the number of gold grains in till is a widely used exploration technique in glaciated terrain. Nonetheless, there is no existing reference material to assess the quality of either the recovery or the counting of gold particles in samples from glaciated sites. We manufactured a reference material (AuGC-1) by mixing a gold-free heavy mineral concentrate with gold grains recovered from mine tailings. The reference material contains 38 gold grains, with a standard deviation of 10 grains, in 1.5 g of heavy mineral concentrate. Of the 38 gold grains, 24 grains are >50 μm (optically determined), and 14 grains are <50 μm (using SEM). This grain concentration is considered optimal, well above Canadian Shield till background and sufficient for obtaining reproducible statistics. Gold grain reference materials can be improved further by ensuring that i) the grain size distribution of gold is similar to that of the matrix; ii) gold grains in the sample originate from different sources or a source having a great variation in gold grain composition; iii) a slightly larger number of gold grains are present in the samples (closer to 50 grains) to reduce the standard deviation; and iv) a larger sample size is used to improve mixing of the high-density gold grains with the mineral matrix.
Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.
Semantic-Aware Video Style Transfer Based on Temporal Consistent Sparse Patch Constraint

This paper proposes a practical style transfer method to synthesize a temporally smooth video whose style information is semantically consistent with the reference video. Due to the lack of paired videos for training, we extend the structure of CycleGAN with sparse patch and temporal constraints, including a new semantic patch loss and a novel temporal loss. Our approach’s key insights are: (1) the semantically paired sparse patches chosen from synthesized videos and reference frames would promote the semantic meaning of style transfer, the preservation of video content, and the smoothness of results by minimizing the discrepancies between these paired patches. (2) the forward and backward temporal consistency among neighbouring frames can reduce the discontinuity in the synthesized video. Extensive quantitative and qualitative experiments on various metrics demonstrate the superiority of our method over state-of-the-art strategies.
Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.
Anatomy of Blockchain Implementation in Healthcare

Blockchain is one of the leading technologies that have a huge number of implications in solving real time problems, especially in sectors like Healthcare, Banking, Aviation, Telecommunication, and so forth. Blockchain is known for its features like interoperability, improved information security, Data integrity, immutability, distributed database, Peer to Peer transaction Network, traceability, and transparency along with a trustworthy environment that makes it more secure and reliable. It is enormously known for decentralizing data, easy accessibility, and management of operations, which makes blockchain a better technology to work on. Currently, the big sectors like healthcare that have a huge number of implications in comparison to others, need to make their activities effective and efficient on a real time basis. Unlike other technologies that are presently available in the healthcare industry, Blockchain technology facilitates many things such as drug supply chain management by making it counterfeit-free, providing interoperability in patient health records, allowing data operations to be safe and precise, enhancing medical insurance security, assistance in disease predications, etc. Ensuring absolute encryption security by way of cryptographic algorithms with healthcare taking pleasure in the stack of benefits. Blockchain can solve numerous problems in healthcare and adapting it, will not only boost the working productivity but also enhances the quality of outcomes with a progressive approach. This chapter focuses on blockchain introduction followed by its implementations in the healthcare industry and issues that could be solved using blockchain technology.
Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution, which is considered the best compromise among the candidates, is known as complex problem-solving.Smart city administrators face different problems of complex nature, such as optimal energy trading in microgrids and optimal comfort index in smart homes, to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service (CPSaaS) based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem's solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular, making it robust against faults and easy to maintain.The proposed architecture's evaluation results highlight its strengths in fault tolerance, accuracy, and processing speed.
Progressive multi-scale fusion network for RGB-D salient object detection

Salient object detection (SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks. The depth map could provide additional spatial prior and boundary cues to boost the performance. Combining the depth information with image data obtained from standard visual cameras has been widely used in recent SOD works, however, introducing depth information in a suboptimal fusion strategy may have negative influence in the performance of SOD. In this paper, we discuss about the advantages of the so-called progressive multi-scale fusion method and propose a mask-guided feature aggregation module (MGFA). The proposed framework can effectively combine the two features of different modalities and, furthermore, alleviate the impact of erroneous depth features, which are inevitably caused by the variation of depth quality. We further introduce a mask-guided refinement module (MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection. Experiments on five challenging benchmarks demonstrate that the proposed method outperforms 11 state-of-the-art methods under different evaluation metrics.
Undetectable Adversarial Examples Based on Microscopical Regularization

Recent works have demonstrated that neural networks are vulnerable to adversarial examples. Although existing methods have achieved satisfactory attack success rates, most adversarial examples can be detected by statistical analysis and further removed. In previous methods, adversarial perturbations are added using adversarial loss and distance metrics, in which the positions of modified pixels are not considered. In this paper, we elaborate a microscopical regularization that introduces adversarial perturbations onto rich texture regions. The microscopical regularization is used to evaluate pixel-level differences between a normal image and its adversarial version. We further propose a novel optimization strategy of modification probability matrices to minimize the loss function that satisfies the restriction of L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> . Through extensive experiments, we show that our method can resist statistical analysis by a large margin and achieve better visual quality than others. The proposed microscopical regularization can also be combined with existing approaches to enhance the undetectability and robustness.
A Machine Learning Based Regulatory Risk Index for Cryptocurrencies

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Individual Identification Method of Leopard in Multiple Scenarios

Individual identification of rare animals in different scenarios helps to accurately grasp the number and scale of animal populations, so as to formulate appropriate protection measures and policies, which are beneficial to maintaining human ecological balance and sustainable development. In this paper, we study the individual identification methods of leopard in multiple scenes, obtain the leopard image data through various channels such as zoos and protected areas, and construct a dataset, which obtains 28,751 images of leopard. In order to extract more fine-grained features of the leopard, Res2net was added to the network, at the same time, the self-attention mechanism was added to improve the global correlation. A R2NMGN method based on the self-attention mechanism was proposed. The experimental results show that the method proposed in this paper is significantly better than other methods in individual recognition of leopard.
A linear method for camera pair self-calibration

We examine 3D reconstruction in unordered sets of uncalibrated images. We introduce a linear method to self-calibrate and find the metric reconstruction of a camera pair. We assume unknown and varying focal lengths but otherwise known internal camera parameters and a known projective reconstruction of the camera pair. We recover two possible camera configurations in space and use the Cheirality condition, that all 3D scene points are in front of both cameras, to disambiguate the solution. Towards identifying camera configurations that would perplex solution disambiguation, we show in two Theorems, first that the two solutions are in mirror positions and then the relations between their viewing directions. We validate our approach in synthetic and real scenes. In camera pair self-calibration and metric reconstruction, our method performs on par (median rotation error Δ R = 3 . 49 ° ) with the standard approach of Kruppa equations followed by 5P algorithm ( Δ R = 3 . 77 ° ). We get realistic multi-view reconstructions, using numerous camera pair metric reconstructions generated by our linear method, rotation-averaging algorithms and a novel method to average focal length estimates. • We introduce a linear camera pair self-calibration and metric reconstruction method. • Our method upgrades a projective reconstruction assuming varying focal lengths. • We describe in two theorems the relative orientation of the camera-solutions recovered. • We introduce a focal length averaging method to improve the accuracy of our estimates. • We get multi-view reconstructions averaging multiple rotation and f. length estimates.
Web Service Clustering Method Based on Word Vector and Biterm Topic Model

The increasing number of Web services brings challenges for accurate and efficient service discovery. Service clustering technology can narrow the retrieval range of service discovery and improve the efficiency of service discovery. However, due to the short text features with few words and difficult feature extraction in service description text, the traditional text clustering model can not get effective clusters. In this paper, a web service clustering method based on word vector and BTM (Biterm Topic Model) is proposed. Firstly, the sparse feature problem of short text is alleviated by expanding the word vector. Secondly, the extended service description text is modeled by the BTM based on Gibbs sampling. Finally, the k-means algorithm is used to cluster web services. Compared with LDA (Latent Dirichlet Allocation) and BTM, this method improves the F-Measure of clustering index. Experimental results show that the proposed method can effectively alleviate the problem of sparse service description text features and improve the clustering effect of services.