Reviewerly/exHarmony huggingface dataset:
Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system.
Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

Summary Multiple‐input multiple‐output (MIMO) technology is much significant for achieving high data rates while considering the multiuser communication. For this purpose, the estimation of Channel State Information (CSI) is performed by the pilot. But, the estimated CSI cannot be applied for downlinks while processing an extremely high speed mobile data as it gets outdated because of the rapid channel variation. Estimating the channel in the MIMO systems is highly difficult on using non‐stationary channel characteristics under the high mobility environments. Channel estimation with received signal‐to‐noise ratio (SNR) feedback ensures an effective performance in the practical wireless MIMO systems. This paper plans to develop an intelligent deep learning strategy to solve the channel estimation problem in MIMO system. The main goal of the proposed model is to estimate the MIMO channel coefficients at a transmitter based on the received SNR feedback information from a receiver using the swarm intelligence‐based deep ensemble learning machine (SI‐DELM). For both the scenarios, the SI‐DELM is developed, in which the convolution neural network (NN) with three classifiers is used. Here, the fully connected layers of CNN are replaced by the NN, recurrent neural network (RNN), and deep neural network (DNN). In the SI‐DEL‐based channel estimation, the hyper parameter tuning is performed by the adaptive iteration assisted mixture ratio‐based cat swarm optimization (AIMR‐CSO) for minimizing the mean square error (MSE) and bit error rate (BER) of the estimated channel. Finally, by performing numerical simulations, the efficiency and better performance of the suggested model is revealed by conducting the comparison between proposed and conventional models.
Analysis of Students’ Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection

This paper presents an in-depth study and analysis of students’ role perceptions and their tendencies in classroom education using a visual inspection approach. A multi example learning student engagement assessment method based on a one-dimensional convolutional neural network is proposed. Based on the conceptual composition of student engagement, head posture, eye gaze, and eye-opening and closing states and the most used facial movement units are used as visual features. For feature extraction, the proposed view of relative change features, based on the video features extracted from the Open Face toolset, the standard deviation of the distance between adjacent multiple frames relative to the center point of the three visual features is used as the relative change features of the video. This results in the phenomenon that students are highly motivated in the early stage and significantly increase the rate of absenteeism in the later stage. With the development of information technology injecting new vitality into educational innovation, many researchers have introduced computer vision and image processing technology into students’ online learning activities, and understand students’ current learning situation by analyzing students’ learning status. There are relatively few studies in this area in classroom teaching. Considering the low relative position correlation of the features in the examples, the examples are analyzed using a one-dimensional convolutional neural network to obtain the example-level student engagement, and a multi-example pooling layer is used to infer the student engagement in the video from the example-level student engagement. Finally, the experimental method is used to apply the student classroom attention evaluation detection system to actual classroom teaching activities, and the effectiveness and accuracy of the design of the student classroom attention evaluation detection system are investigated in depth through specific applications and example analysis, and the accuracy of the method of this paper is further verified by communicating feedback with teachers and students in the form of interviews.
Study of IT and Data Center Virtualization

Virtualization and utilization of the hardware and software for an organizational information technology equipment is one of the major cost cutting process and methodology. A virtualized environment creates the centralized solution so that the management of information technology resources are very easy and also needs less number of administrative staff members. In this paper we have presented the study of the effective virtualization technique and process with detailed illustration to enhance the fruitfulness of the virtualization. We have identified the best process method to virtualize the information technology infrastructure and presented the phase and sub phases of the virtualization to implement it effectively to reduce the deployment cost with the efficient functional output having existing information technology resource for an organization in general form. This paper also discuss the analytical explanation of the different technologies for the virtualization to the information technology infrastructure and resources of an organization with best suitability options.
Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images.
Multi-scene small object detection with modified YOLOv4

Abstract Applications of small object detection exist in many different scenarios of our daily life, and this topic is also one of the most difficult issues in studying object detection and recognition. Therefore, improving small object detection accuracy is not only important in theory, but also significant in practice. However, current detection relevant algorithms are inefficient in this task; therefore in this study, a type of generalized improved algorithm based on YOLOv4 model is proposed. After the “ADD” and “Concat” layers of the conventional cross stage partial network (CSPNet), a type of mixed attention module (MA) is added to strengthen the spatial and channel feature information of the object; the dilated convolution module (DC) is then used to expand the receptive field information around the peripheries of target small objects. Several numerical experiments show that this modified model associated with its algorithms achieves higher mean average precision of the tests on the Pascal VOC and the VISDRONE data sets (mAP@0.5). Compared with YOLOv4, these performance assessment indices increase by 3.23% and 2.88% respectively. After refining the YOLOv4 network structure with the condition that the size of input images set as 416*416, the average accuracies of the model can reach 87.58% on Pascal VOC data set and 26.12% on VISDRONE data set. These results present the apparent better performance of this method than the original detection algorithm on recognizing small objects without affecting the real-time performance.
Detection and Localization of Copy-Move Forgery in Digital Images: Review and Challenges

Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions.
In-Depth Evaluation of the Impact of National-Level DNS Filtering on DNS Resolvers over Space and Time

DNS filtering is the practice of blocking access to certain sites for a specific purpose, often content-based filtering. Unlike previous studies that focused on the behavior of national-level DNS filtering itself (e.g., location of filtering devices), we demonstrate and evaluate in depth the impact of DNS filtering on different types (public, ISP, and open) of DNS resolvers in the censored networks. In particular, we actively send DNS queries for 83 well-selected domain names to three types of DNS resolvers and keep track of the resolvers’ responses changing over time and space in China. Here, we present the results of our system running for 40 days, during which we obtained a total of 1.7 billion DNS records. Using these collected data, we found that specific DNS resolvers are unaffected by DNS filtering devices and can respond with the correct IP addresses for particular blocked domains. Furthermore, we revealed that three factors should be considered to evaluate the impact of a country-level DNS filtering mechanism: DNS resolver, client location, and blocked domain. Finally, we propose and implement a system to identify the correct IP addresses of blocked domain names in censored networks based on the characteristics of country-level DNS filtering.
Enhancing Security in IoT Devices by Using Pseudo Random Number Generator Based on Two Different LFSR and A Comparator

The modern era is an era of connected devices. To secure the connection between these devices many different methods are used. Pseudo random generator is a technique used to encrypt the communication between these devices. This paper presents the Linear Feedback shift Register (LFSR) based pseudo random number generator (PRNG) to encrypt the data. Two prime number bit LFSRs are used (LFSR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and LFSR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) with same clock period and a 1-bit comparator is used for final output of the output sequence. The comparator incorporate inequality in the linear recurrence equation of the LFSR which in turn increases the complexity for an adversary to predict the solution. All simulations are done in Vivado 2018.2.
Bio-Inspired Algorithms for Prey Model Optimization(February 2022)

Optimization means making the best use of anything or using resources effectively or achieving high quality under the constraints offered. Or at the lowest cost to achieve the optimum result. This research paper contains the algorithms all of which are inspired by the design of BAT algorithms, firefly algorithms and cuckoo algorithms. Also their improved version to optimize the various problems for example real life problems like the traveling salesman problem, manufacturing process optimization, power system stabilizer, engineering optimization etc. BAT algorithm is a type of algorithm which is on the echolocation behavior of bat and this algorithm is Meta heuristic in nature. The algorithms for the optimizations are highly inspired by the nature, behavior of the different animals, birds, wolves, fireflies, fishes etc. The old methods are actually not very efficient as they have some restrictions while these evolutionary algorithms are solving these problems and finding the optimum solution. Optimization problems are very challenging to solve either those are of single objective or of the multi objective. Also it is said that NP-hard is not possible to solve this it efficiently by any algorithm in an acceptable time period. As there were real world problems which are actually nonlinear. As these problem leads to the complexity, so to solve these types of problems is not an easy task done by any software.
Relevance-guided unsupervised discovery of abilities with quality-diversity algorithms

Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In this work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a Quality-Diversity algorithm that autonomously finds a behavioural characterisation tailored to the task at hand. In particular, our method introduces a custom diversity metric that leads to higher densities of solutions near the areas of interest in the learnt behavioural descriptor space. We evaluate our approach on a simulated robotic environment, where the robot has to autonomously discover its abilities based on its full sensory data. We evaluated the algorithms on three tasks: navigation to random targets, moving forward with a high velocity, and performing half-rolls. The experimental results show that our method manages to discover collections of solutions that are not only diverse, but also well-adapted to the considered downstream task.
Maneuvering Planning for UAVs in Forest Surveillance and Fire Detection Missions with Kinematic Uncertainties

Unmanned aerial vehicles (UAVs) have brought increasing spotlights to the area of forest fire detection, monitoring, and tracking in latest years because of their high flexibility, efficiency, lower cost, and less risk without the need for pilot onboard. Commonly, heavier and more complex tasks can be accomplished by employing multiple UAVs in a formation instead of a single UAV. However, one potential trouble is that the power could constantly exhaust or faults could happen on UAVs in the formation in the field, therefore the weakened UAVs must be supplanted by new vehicles from the UAV base. Thus, the problem is to find an optimal way to navigate the recent UAVs to join the team and to maintain the formation for the remaining task. Additional difficulties arise when uncertainties of motions are encountered over the flight. To overcome these challenges, an uncertainty-embedded maneuvering planning strategy based on the Star-Minimax algorithm is developed. Simulation results prove that a newly assigned UAV can be effectively navigated to join the UAV team, and the formation can be sustained during the flight even in the presence of kinematic uncertainties. Flight experiments, operated in the NAV Lab at Concordia University, further validate the functional performance of the proposed maneuvering planning strategy in real-time.
Recognition awareness: adding awareness to pattern recognition using latent cognizance

This study investigates an application of a new probabilistic interpretation of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism wildly used in classification and object recognition. However, a softmax mechanism forces a model to operate under a closed-set paradigm, i.e., to predict an object class out of a set of pre-defined labels. This characteristic contributes to efficacy in classification, but poses a risk of non-sense prediction in object recognition. Object recognition is often operated under a dynamic and diverse condition. A foreign object-an object of any unprepared class-can be encountered at any time. OSR is intended to address an issue of identifying a foreign object in object recognition. Softmax inference has been re-interpreted with the emphasis of conditioning on the context. This re-interpretation and Bayes theorem have led to an approach to OSR, called Latent Cognizance (LC). LC utilizes what a classifier has learned and provides a simple and fast computation for foreign identification. Our investigation on LC employs various scenarios, using Imagenet 2012 dataset as well as foreign and fooling images. Its potential application to adversarial-image detection is also explored. Our findings support LC hypothesis and show its effectiveness on OSR.
Containerless Plurals: Separating Number from Type in Object-Oriented Programming

To let expressions evaluate to no or many objects, most object-oriented programming languages require the use of special constructs that encode these cases as single objects or values. While the requirement to treat these standard situations idiomatically seems to be broadly accepted, I argue that its alternative, letting expressions evaluate to any number of objects directly, has several advantages that make it worthy of consideration. As a proof of concept, I present a core object-oriented programming language, dubbed Num , which separates number from type so that the type of an expression is independent of the number of objects it may evaluate to, thus removing one major obstacle to using no, one, and many objects uniformly. Furthermore, Num abandons null references, replaces the nullability of reference types with the more general notion of countability, and allows methods to be invoked on any number of objects, including no object. To be able to adapt behavior to the actual number of receivers, Num complements instance methods with plural methods, that is, with methods that operate on a number of objects jointly and that replace static methods known from other languages. An implementation of Num in Prolog and accompanying type and number safety proofs are presented.
Human Emotion Detection Using Open CV

Face recognition from picture or motion picture could be a standard point in bioscience analysis. This well called the facial detection, contend a vital play in closed-circuit entertainment because it doesn&#x0027;t want the object&#x0027;s sync. As face could be a variable thing having highest degree of frequency change its look, which creates facial recognition a tough downside in tablet display. During this, performance and speed of identification could be a major problem. Main objective of this research is to sight object of interest in real time and to stay chase of constant object supported camera and picture set rule by means of Open CV (a python library) and Python language. The technique includes 3 parts: recognition module, coaching module, identification library.
Framework for securing EPR records with Searchable encryption &amp; AES

Electronic Patient records (EPR) are very sensitive to individuals, so it becomes very important to manage the security with preciseness. Recent attacks show that improper security management to EPR causes a serious violation of HIPPA by seeking Protected Health Information. Managing the security of EPR records is very time and cost constraint, so we need cost-efficient methods for securing the EPR records. EPR records have attributes that need various levels of security, so to encrypt the whole EPR with the same encryption algorithm is a wastage of time and power. To overcome this issue, we proposed a framework that first classify the EPR attributes into three class and then employs the appropriate encryption method to that attribute based on the class. The proposed framework reduces the burden of encryption time and cost.
Image Semantic Space Segmentation Based on Cascaded Feature Fusion and Asymmetric Convolution Module

With the rapid development of deep convolutional neural networks, the results of image semantic segmentation are remarkable, and the segmentation effect is greatly improved. The pooling layer of the convolutional neural network will reduce the resolution of the feature map, which makes the convolutional neural network lose a lot of spatial information while extracting semantic features. How to integrate semantic features with semantic information and spatial information will become an important factor to improve the performance of semantic segmentation. Firstly, this paper improves the global attention upsampling module and uses the improved global attention upsampling module to form a multiscale global attention up-mining module in a new connection way. The upsampling module of multiscale attention establishes the relationship between high-level features and lower-level features at a longer distance. Compared with PANet, the method proposed in this paper deepens the close relationship between semantic information and spatial information. Experiments show that the segmentation effect of the feature fusion method based on cascade is better than that of the feature fusion method based on weight. The segmentation effect of the two fusion methods is improved by 8.3% and 5.7% compared with the PANet on the PASCAL VOC 2012 dataset and by 4.5% and 3.6% on the Cityscapes dataset, respectively. The research results of this paper make the high-level semantic information and shallow feature information cooperate to improve the segmentation effect.