Reviewerly/exHarmony huggingface dataset:
Construction of the Convolutional neural network Based on the increase optimizers provided and atomic layers provided

In order to solve the problem of low accuracy of traditional neural network code test set and verification set, we use VGG network structure to deal with it. Its main feature is that it has a simple structure and replaces all convolution kernels with 3 × 3. Compared with the pooled cores of Alexnet, VGG uses 2 × 2 pooled cores. Number of parameters is large, and most of the parameters are concentrated in the full connection layer. Proper network initialization and use of batch normalization layer are very important for training deep network. In Alexnet, there is only one convolution in each convolution layer, and the convolution kernel is 7*7. In VGGnet, each convolution layer contains 2 ~4 convolution operations. The most obvious improvement of VGGnet is to reduce the size of convolution kernel and increase the number of convolution layers. Based on the traditional convolutional neural network, we train a better classifier to realize the recognition and classification of cifar-10 data sets. The accuracy of verification set is improved from 52.9% to 70.3%, and the accuracy of test set is improved from 54.7% to 71.5%.
Detecting False Data Attacks Using KPG-MT Technique

The change of customary energy organizations to savvy lattices can help with reforming the energy business regarding unwavering quality, execution, and reasonability. Notwithstanding, expanded availability of force network resources for bidirectional correspondences presents extreme security weaknesses. In this letter, we explore Chi-square indicator and cosine comparability coordinating methodologies for assault discovery in savvy lattices where Kalman channel assessment is utilized to quantify any deviation from real estimations. The cosine likeness coordinating methodology is discovered to be strong for identifying bogus information infusion assaults just as different assaults in the savvy lattices. When the assault is identified, framework can make a preventive move and alert the administrator to make a safeguard move to restrict the danger. Mathematical outcomes acquired from recreations substantiate our hypothetical investigation.
Old English class I strong verbs lemmatisation: a morphological generation approach

This article takes issue with type-based automatic lemmatisation of the Old English class I strong verbs L-Y. To reach this goal, the article discusses the design of a Morphological Generation interface implemented with the rules of Old English strong verb inflection as well as with the most frequent variant spellings. The forms provided by the interface are automatically checked against the two most representative corpora of Old English, the Dictionary of Old English Corpus and the The York-Toronto-Helsinki Parsed Corpus of Old English Prose, and validated results are assigned the corresponding lemma. The results prove that almost 99% of the validated forms can successfully be assigned lemma. The remaining 1% correspond to formally ambiguous inflectional forms, where lemma assignment is in dispute between two candidates. The conclusion is reached that disambiguation is only possible on the basis of contextualised token-based analysis. The article confirms that type-based lemmatisation of Old English strong verbs can be largely automatised.
Threat Landscape as an Information Security Analysis Means

A landscape of information threats, its place in the information security, problems of its formation and analysis are considered in this work. An analogy is drawn with the software testing principles and its application to search for threats to information security and use in the landscape formation. A methodology to form a threat landscape is represented. The data sources and their features are examined to form the threat landscape. It is suggested to categorize the threats by the direction of attack, type, target and danger. The application of the methodology for building a threat landscape is considered on the example of a small office infrastructure. To visualize the landscape, it is proposed to use scatter diagrams. By grouping threats by direction, type and magnitude, it is possible to identify the main areas of threats in the landscape, which is shown by an example. The conclusion considers the formation problems of a threat landscape, as well as its application for analyzing the attack chains.
Developing a Reinforcement Learning Algorithm to Model Pavlovian Approach Bias on Bidirectional Planning

Developing a Reinforcement Learning Algorithm to Model Pavlovian Approach Bias on Bidirectional Planning
A Study on Review of Application of Blockchain Technology in Banking Industry

Financial innovations, economic transformation based on development of the Internet on one side, and the issues of transparency, security, and integrity of data on the other are the most important concerns in digital transformations. Banking industry is passing through metamorphosis as the customer is more digitized. This paper highlights Blockchain technology—the concept, need, architecture, and application in the banking industry. To validate the application of Blockchain technology in the banking industry primary data has been collected through a structured questionnaire to collect the perceptions of the stakeholders. The results confirmed that the new technology is going to provide promising clear and transparent transactions with the Distributed Ledger System.
Application of Artificial Intelligence in Customer Service Field

As the demand for customer service continues to increase, more and more companies are trying to apply artificial intelligence technology in the field of customer service, enabling customer service through intelligent customer service, reducing customer service pressure and reducing operating costs. Currently, the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions, and it is difficult to understand complex user expressions. In order to solve the problem of low accuracy of multi-round dialogue language understanding, this paper proposes a language understanding model based on the fusion of CNN and Attention. The model builds an “intention-slot” joint model based on the “encoding-decoding” framework, and uses hidden semantic information that combines intention recognition and slot filling, avoiding the problem of information loss in traditional isolated tasks, and achieving end-to-end language understanding. At the same time, an improved attention mechanism based on CNN is introduced in the decoding process to reduce the interference of redundant information in the original text, thereby improving the accuracy of language understanding. Finally, by applying the model to intelligent customer service, it is verified through experimental comparison that the proposed fusion model has a good effect in improving the performance of intent recognition and slot filling, and can improve the user experience of intelligent customer service.
Artificial and Real-World Chaotic Oscillators

Four archetypal chaotic maps are used to generate the noise-free synthetic datasets for the forecasting task: the logistic and the Hénon maps, which are the prototypes of chaos in non-reversible and reversible systems, respectively, and two generalized Hénon maps, which represent cases of low- and high-dimensional hyperchaos. We also present a modified version of the traditional logistic map, introducing a slow periodic dynamic of the growth rate parameter, that includes ranges for which the map is chaotic. The resulting system exhibits concurrent slow and fast dynamics and its forecasting represents a challenging task. Lastly, we consider two real-world time series of solar irradiance and ozone concentration, measured at two stations in Northern Italy. These dynamics are shown to be chaotic movements by means of the tools of nonlinear time-series analysis.
Planned Maintenance Queue Simulation based on Failure Probability: Focused on the Naval Battleship

The constant maintenance interval performed by ROK Navy ships has the advantage of convenient maintenance planning. In the past study, the failure rate of the ship continued to change with the life of the ship. Planned maintenance performed at regular intervals may not be appropriate to maintain the varying amount of failure of ships. Probability-based maintenance considers the changing amount of ship failure. Although the effect of probability-based maintenance was derived from previous studies, it was studied based on a single ship. Therefore, in this study, the effect of applying probability-based maintenance to multiple ships was confirmed as in reality. When maintenance was performed according to probability, the maintenance time overlapped and queuing occurred. The system dynamics simulation was performed considering the advantages of applying probability-based maintenance and the disadvantages due to queuing at the same time.
Using a constraint-based expert model to provide step-level feedback for user-inputted mathematics equations

Studies across a variety of educational fields have shown the efficacy of feedback on student performance and learning. Web-based homework is a common feature of secondary and collegiate mathematics courses to provide such feedback. While web-based homework provides often instantaneous feedback to students as they complete assignments, the feedback is limited to the correctness of the final answer or to a limited number of pre-programmed common mistakes. Intelligent Tutoring Systems have the ability to provide step-by-step feedback and guidance tailored to individual students, however authoring content for such systems is generally considered a huge barrier to their wider use. This paper presents an alternative system, called SANYMS (Show and Name Your Math Steps), which combines the wide-scale usability of web-based homework with a Constraint-Based Intelligent Tutoring System Expert Model based on the open-source computer algebra system Maxima. With the integration of Maxima, SANYMS can provide step-by-step feedback to students constructing their own solutions to a wide variety of algebraic equations of their choice without the need for extensive content authoring inherent in many Intelligent Tutoring Systems.
3D Orthomosaic From Unmanned Aerial Vehicle (UAV) Image Data For Real-World Environment 3D Map Creation Autonomous Vehicles Simulation System

Testing is necessary to prove the reliability of autonomous vehicles (AV) statistically and to reduce fatal accidents. However, the manual testing process takes a long time and be expensive. One of the solutions to address this issue is to use a simulation system. Thus, it is necessary to create a virtual environment that includes roads, pedestrians, and buildings. The purpose of this research is to find an appropriate and affordable way to create 3D maps and data drive simulators based on the real-world environment. This research is conducted using an experimental method to find out how the 3D orthomosaic effect of unmanned aerial vehicle (UAV) from a drone that has been further processed through the RoadRunner and CARLA/Unreal engine simulation software (Independent Variable) to produce a simulation map for AV (dependent variable). Results of this research prove that the 3D orthomosaic image data generated by the UAV, which was processed through the RoadRunner and CARLA/Unreal engine simulation software, could be used properly in making 3D maps of the real-world environment to become the data drive of the AV simulator.
Towards Transferable Adversarial Examples Using Meta Learning

With the discernment of the vulnerability of deep neural networks recently, adversarial attack methods have become one of the hot spots for the security of artificial intelligence technologies. While previous researches can effectively generate adversarial examples in white-box attacks, it remains challenging to transfer these adversarial examples to black-box models, where the attacker has no knowledge about the model structure and parameters. This paper focuses on the transferability of adversarial examples and proposes a novel approach named Model-Agnostic Attack (MAA), in which meta-learning is explored to facilitate the transferability of adversarial examples crafted on vanilla adversarial attacks across diverse black-box models. Specifically, model-agnostic meta-learning, a meta-learning approach, can train a well-generalized model to various unknown tasks and is utilized to alleviate the overfitting problem of adversarial examples for the specified models, so that the adversarial examples can be easily transferred to black-box models. Besides, we highlight that the MAA is a plug-and-play approach and can be effortlessly integrated with any existing technologies to further boost transferability. Extensive experiment results on CIFAR-10 and CIFAR-100 exhibit the superiority of MAA that achieves higher transferability than state-of-the-art methods on average against black-box models.
Secure Outsourcing of Image Editing Based on Homomorphic Encryption

As cloud computing has become more popular and easier to access, many online applications now provide image storage and editing services to their users. However, an image may contain personal information which the user does not want disclosed. In this paper, we focus on secure outsourcing of image storage and editing to an untrusted cloud environment using homomorphic encryption. We use Paillier’s Homomorphic Cryptosystem to encrypt pixels of an image and then convert an existing image editing method into one that can operate over the encrypted pixels. The homomorphic property is showcased by implementing a brightness transform function that can operate over the encrypted image without revealing the original image data.
Automatic Shoe Detection Using Image Processing

Image processing is a technique for applying operations on an image in order to improve it or extract relevant information from it. Object detection is a computer vision approach for identifying and locating things in images and videos. Deep learning has showed enormous promise in a wide range of real-world applications. Recent object detection based on image processing and deep learning models has yielded promising results in terms of object detection in images. Educational institutes and industries are insisting students and employees to wear shoes to enhance their safety and as well as for the professional appearance. It is observed that some of the students or employees are not wearing shoes which may lead to some serious injuries at labs and work places . To avoid this institutions and industries are involving with the physical verification process. The physical verification process is overhead to them. The intension of the proposed work is to automate the shoe detecting process using image processing and deep learning techniques. A shoe detection dataset consists of with shoe images which has been used to train model. The trained model is used to identify whether the person is wearing shoes or not in real time. If the person is not wearing shoe then the appropriate notification will be given. This proposed work will be helpful to the industries or institutions to ensure the safety measures.
RDMAbox: Optimizing RDMA for Memory Intensive Workload

We present RDMAbox, a set of low level RDMA optimizations that provide better performance than previous approaches. The optimizations are packaged in easy-to-use kernel and user space libraries for applications and systems in data centers. We demonstrate the flexibility and effectiveness of RDMAbox by implementing a kernel remote paging system and a user space file system using RDMAbox. RDMAbox employs two optimization techniques. First, we suggest RDMA request merging and chaining to reduce the total number of I/O operations to the RDMA NIC. The I/O merge queue at the same time functions as a traffic regulator to enforce admission control and avoid overloading the NIC. Second, we propose Adaptive Polling to achieve higher efficiency of polling Work Completion than existing busy polling while maintaining the low CPU overhead of event trigger. Our implementation of a remote paging system with RDMAbox outperforms existing representative solutions with up to 4x throughput improvement and up to 83&#x0025; decrease in average tail latency in bigdata workloads, and up to 83&#x0025; reduction in completion time in machine learning workloads. Our implementation of a user space file system based on RDMAbox achieves up to 5.9x higher throughput over existing representative solutions.
An Efficient Data Management based on attribute in Crypt Cloud

Nowadays Huge volume of data is stored in the public cloud by the proprietors in various sectors. As these data is highly confidential they need to integrate cloud together with a particular set and encryption of characteristics to get into command on the cloud data. While uploading the data into the public cloud it will be assigned a bit of attribute ready to the data of theirs. In case any of the authorized cloud person really wish to retrieve back his own data, they need to type the specific feature placed to carry out additional steps on Data owner’s information. A cloud person really wants to register the details of theirs below the cloud group to use the Data owner’s information. Owners wish to publish the details of theirs as characteristics together with the designation of theirs. In line with the person specifics, Semi Trusted Authority makes decryption secrets to obtain command on the owner’s information. An end-user is able to conduct a great deal of businesses with the cloud Data. The attribute-based security schemes for managing cloud data in our suggested system and attaining the security level when compared to the present system.
An Algebra Supporting Semantic Query for Graph Data Model

Following the massive emergence of graph data such as the RDF graph and the knowledge graph, semantic query of graph data has become a research hotspot. However, most of the existing studies suffer from lower query efficiency of complex semantics and higher query rewriting cost, and so on. Moreover, these studies mainly improve the query efficiency of graph data from the application perspective, with less research on the related query theory. Query theory is a robust basis for realizing queries and their applications. In this paper, we present an algebra supporting semantic query for graph data model, firstly defining a generic graph data model on the basis of common graph models. Then proposing basic query operations and keyword query methods. Finally, proposing association query methods and query optimization strategies. The graph query algebra study in this paper has some reference value for the research on semantic query of graph data.
Analysing Gender and Age Aspects of Cyberbullying through Online Social Media

In this paper, we focus at tracking down cyberbullies and categorize them based on their age and gender. The dataset that we use to analyze this information is provided by the MySpace group data labeled for cyberbullying. Machine learning classifiers are trained using this data to detect cyberbullies and later we analyze the age and gender patterns of those cyberbullies. We look for features that are simple to extract as well as yield good outcomes. As appropriate training data is often tough to obtain in machine learning-specially in the domain of cyberbullying detection - we also examine to what extend does lesser amounts of training data would contribute to better outcomes by performing cross-validation. Our findings show that employing a few yet expressive features has a significant benefit in detecting cyberbullies, particularly when size of training data is small.
Animating Still Natural Images Using Warping

From a single still image, a looping video could be generated by imparting subtle motion to objects in the image. The results are a hybrid of photography and video. They contain gentle motion in some objects, while the rest of the image remains still. Existing techniques are successful in animating such images. However, there are still some drawbacks that need to be investigated, such as too-large computation time necessary to retrieve the matched videos or the challenges of controlling the desired motion not only in terms of a single region but also in terms of consistency in regions. In this work, we address these issues by proposing an interactive system with a novel warping method. The key idea of our approach is to utilize user’s annotations to impart motion to certain objects. With two proposed phases in terms of preserve-curve-warping and cycle warping, a looping video is generated. We demonstrate the effectiveness of our method via various experimental challenging results and evaluations. We show that with a simple and lightweight method, our system is able to deal with animating a still image’s problems and results in realistic motion and appealing videos. In addition, using our proposed system, it is easy to create plausible animation using simple user annotations without referencing the video database or machine learning models and allows ordinary users with minimal expertise to produce compelling results.
Mixed State Parametrization and Two-Qubit Entanglement

A generic scheme for the parametrization of mixed state systems is introduced, which is then adapted to bipartite systems, especially to a 2-qubit system. Various features of 2-qubit entanglement are analyzed based on the scheme. Our approach exploit much the interplay between is marked by pure states as Hilbert space vectors and mixed states as density matrices, both for the formulation of the parametrization and the analysis of entanglement properties. Explicit entanglement results, in terms of negativity and concurrence, for all 2-qubit mixed states with one single parameter/coordinate among the full set of fifteen being zero and a few other interesting cases areâ€¨ presented.
Study On Device and Method of Plant Pest Control by Pulse High-Voltage

Based on modern power electronic high-frequency switching power supply technology and pulse power technology, a high-voltage pulse power supply and discharge control system are designed to kill pests in soil or trees. The best working conditions of the equipment are determined by the electric discharge experiment of organisms (mice, representing mammals, simulating underground rodents and other pests; ants, representing insects, simulating termites and other pests). Compared with commonly used physical and chemical methods, the pest control effect of this method has the advantages of portability, low cost, safety, etc., which can maximize the prevention and control efficiency, protect the ecological environment, and effectively guarantee the survival rate of plants.