Reviewerly/exHarmony huggingface dataset:
Innovation of College English Teaching Mode Based on Computer

In recent years, the development of computer is more and more rapid, and has been widely used in various fields. In the rapid development of computer environment, the application of computer has become an important development trend of all walks of life. At the same time, computer has also brought innovation to college English teaching. Compared with the traditional college English teaching system, computer intervention can make up for the shortcomings of traditional teaching methods and bring new changes. This paper first briefly introduces the current situation of College English teaching, and then conducts an in-depth investigation and Research on the current situation of College English Teaching in China by means of literature survey and questionnaire survey. Through the survey data, it is found that there are problems such as the solidified teaching content, the single teaching content and the teaching mode. Finally, based on the current domestic environment, this paper puts forward some suggestions for College English teaching reform in China.
Book Graphics by Nikolay Pavlovich Akimov. To the 120th Birth Anniversary of the Artist

The article is devoted to the art work of the outstanding Russian artist, stage director, theatre figure Nikolay Pavlovich Akimov. Using bibliological, art history and source study methods, the author analyzes the least known works of the master in the field of book graphics and posters, which have been overlooked by most researchers. The article traces the relationship between the stage director’s graphic experiments and his theatrical work. The author disputes the statement that most of Akimov’s covers of the 1920s belong to the “illustrative type”, which is often found in the bibliological literature, and emphasizes the generalized, symbolic, conditional nature of the portraits of literary characters. The examples show the coincidences and differences between the poster interpretation of the characters’ images and their book interpretation. The article considers not only the early works of the master, but also his design experiences of the late 1950s — early 1960s. The author reveals the most significant features of Akimov’s graphics: strongly pronounced fantasy spirit, the artist’s predilection for the eccentric and grotesque, for unexpected angles and lighting effects, distortion of real proportions for the sake of sharpening the character of the image, the inclusion of letters in the structure of figurative compositions.
Research on Predictive Control of Students' Achievements in Physical Education Class and Physical Education Class Universities Based on Mathematical Model Analysis of Multiple Linear Regression Equations

The academic achievement is influenced by many factors, which is an important basis for teachers and teaching management departments to analyze and study the teaching reform and improve the teaching quality. In many economic forecasting problems, there are often more than one factor affecting the forecast object, and the influence of these factors on the forecast object is linear or nonlinear. For nonlinear regression, it is often transformed into linear regression through appropriate transformation. According to the multivariate linear regression analysis method in multivariate linear statistical analysis, the test score indicators are quantified, and the regression model of test score evaluation and prediction is established, which can effectively predict the ranking of students. Through comparative analysis, the application conditions of this method are explained, and the effectiveness of this method is verified. Finally, the specific ways of applying this method to teaching are analyzed.
Secure and Lightweight User Authentication Technique for IoT Devices

The Internet of Things for data exchange (IoT) is the new age of communication technology. As the use of smart devices increases significantly, IoT services are becoming more accessible. Mutual authentication scheme and session key communication shows an essential role in ensuring secure data transmission between the IoT infrastructures and remote consumers. Security analysis is an essential part of testing against relevant security threats. It uses the AVISPA simulation tool to verify a security-specific work. Accelerate the expansion of millions of devices-FT-Generation (5G) cellular networks connected from device to device (IoT). To resolve these security vulnerabilities, this paper proposes a state-of-the-art three-factor user visibility scheme. The proposed scheme provides the desired aspects for the IoT environment, and its computational and communication costs are compatible with low-cost IoT devices. Contains solutions to significant distribution issues in the MTC environment. Besides, this method of authenticated and decentralized group vision is used in a variety of situations. The proposed approach addresses the need for faster automation of advanced Agile 6G networks supported by most networking IoT devices. The rapid expansion of machine and device, the use of a wide variety of smart applications such as Smart e-Healthcare, Smart Education, Intelligent Transport Systems, is proliferating. In these applications, various devices are identified using radio-frequency identification (RFID) tags. Data collected in different geographical locations used to transfer device application data to other remote or local objects. Although, as mentioned earlier, applications need different visualization levels to automatically read data stored in RFID tags embedded in an object/device. It leads to fake issues. Besides, RFID-based visualization carries a heavy computational burden, as RFID-tags have lower computational efficiency. For example, symmetry key cryptography has several anonymous RFID-based visual heuristic schemes designed with hash functions and lightweight cryptographic tools to solve these issues. Although, all of these works fail to meet the known safety and operational requirements. The proposed work provides RFID-based lightweight tags and secures more privacy, obscurity and inaccessible localization.
Element mobility as a proxy for delineating shear zone boundaries in orogenic belts: A case study of Iwaraja shear zone, southwestern Nigeria

Shear zones are localised regions of intense deformation and are important archives of element mobility history during crustal deformation. The boundary is determined by examining shear deformation fabrics in affected rocks. However, the "mixture" of relicts of undeformed rocks coupled with newly formed shear fabrics affect the determination of the internal boundaries of the shear zone and its boundary with the unsheared protolith, which in turn limits the evaluation of the shear zone's width. A new approach involving element mobility across the shear zone is proposed as a proxy to address this problem and corroborate existing methods. The petrographic and geochemical analyses of granitic gneiss across the Iwaraja shear zone and its adjacent rock were carried out. The element mobility patterns and volume of chemical changes were evaluated using isocon and Cartesian element-distance plots to interpret the chemical changes in the granitic gneiss across the shear zone. The result indicates a very high enrichment of TiO2, Fe2O3, MnO, MgO, P2O5, and REEs with relatively similar mobility patterns and a high depletion of Na2O, Rb, and Th in the shear zone. Al2O3 is not significantly mobilized but the mobility pattern of K2O is erratic. Based on the chemical changes in the granitic gneiss, the ductile shear zone is a CaO–MgO–Fe2O3-REE-enriched and Na2O–Rb–Th-depleted system. Under greenschist facies condition, the chemical mobility of TiO2, MnO, P2O5, Th, Rb, Zn, Ba, Zr, Th, Hf, and REEs is sensitive to the changes in the deformation intensity between the strongly- and weakly-deformed zones whereas REE [Lu, Yb, Gd, Eu, Ho, Tb] is useful to determine the boundary between the weakly-deformed zone and the unsheared rock. These index minerals are useful in other similar ductile shear zones in any orogenic belt. Based on the element mobility patterns, the width of the affected granite gneiss in the shear zone is ~680 m.
Individual Differences on Conservative and Risky Behaviors about Information Security

In order to provide information security; hardware and software solutions are widely used; research and development endeavors increases day by day and huge amounts of investments are made. However, these attempts still cannot stop information systems’ to be compromised because of the holes in the human firewall caused by vulnerable behaviors of individuals. Even though individuals have knowledge about information security, they do not always show appropriate behavior. Hence information security is not a problem that can only be solved with technological solutions. As being the weakest link, human behavior on information security needs to be evaluated and assessed. With this study it was aimed to examine the relationship between conservative and risky behaviors of individuals about information security and individual differences which are demographics, internet usage routines, personality, risk perception and exposure to offense. Behaviors and individual difference variables were examined via a survey of 619 participants who were invited through social media platforms. Multiple linear regression analysis conducted and one linear model was created in order to calculate the amount of change on conservative and risky behaviors caused by independent variables. While level of education, age, duration of being an internet user, time spent on the internet, agreeableness, neuroticism, openness, exposure to offence and risk perception variables were found as significant predictors for risky behaviors; time spent on the internet, agreeableness, conscientiousness and openness variables were found to be the significant predictors for conservative behaviors. The results of the study can be used either by organizations or educational institutes for developing personalized and adaptive training programs or for creating preventive strategies.
DL Inference and Training Optimization Towards Speed and Scale

The application of deep learning models presents significant improvement to many services and products in Microsoft. However, it is challenging to provide efficient computation and memory capabilities for both DNN workload inference and training given that the model size and complexities keep increasing. From the serving aspect, many DL models suffer from long inference latency and high cost, preventing their deployment in production. On the training side, large-scale model training often requires complex refactoring of models and access to prohibitively expensive GPU clusters, which are not always accessible to many practitioners. We want to deliver solid solutions and systems while exploring the cutting-edge techniques to address these issues. In this talk, I will introduce our experience and lessons from designing and implementing optimizations for both DNN serving and training at large scale with remarkable compute and memory efficiency improvement and infrastructure cost reduction.
Question Answering System Using Knowledge Graph Generation and Knowledge Base Enrichment with Relation Extraction

Question answering system is one of the predominant application that used to get the answer for the input natural language question. It reduces the search time to find the answer for particular question. In order to answer a natural language question, question understanding plays a vital role. Understanding a question semantically, leads to get the intention of the input question accurately. In order to understand the question, semantic query graph will be constructed to analyze the question phrases in a structured way by extracting entity and relation phrases. If there is no relevant relation phrases in knowledge base, then query graph cannot be constructed. In order to overcome this unavailability of relation phrases, using n-gram-based attention model and joint learning, relation phrases will be extracted. Using encoder-decoder model, extracted relation phrases along with that corresponding entity and predicate phrases will be converted as triples with unique IDs. Finally, using top-k sub–graph matching algorithm, the answer with the top score will be selected as the final answer. In this proposed question answering system with relation enrichment (QARE) using two datasets such as QALD5 and web questions benchmark, comparative analysis had been carried out with two existing systems. Experimental results show that proposed system achieves better performance than existing systems.
Implementation and quality measures of graph theory model based image segmentation process in medical application

Abstract This research work simplified the representation of an image into more significant and easier way to analyse the image segmentation process by applying graph theory using color spatial clustering with consensus region merging. The color spatial clustering with consensus region merging is compared with other traditional and graph theory model to analyse the various quality measures calculated for the input of magnetic resonance imaging (MRI) scan and X-Ray images which will be useful in medical imaging for better analysis during diagnosis. From quality measures, the proposed method shows good quality image parameters as it has lower MSE, NAE values.
The Human Cause

This paper argues that, although AI machines are increasingly able to produce outputs that facially qualify for copyright or patent protection, such outputs should not be protected by law when they have no identifiable human cause, that is, when the autonomy of the machine is such that it breaks the causal link between the output and one or more human creators or inventors. As a species, normatively we should seek to preserve incentives for human creativity and inventiveness, as these have been hallmarks of the higher mental faculties often used to define humanness. The paper also discussed situation where human and machines work together and how courts can apply the proposed approach.
Application of Multiple Neural Networks in Aspect Sentiment Analysis

Sentiment Analysis (SA) has been an important part of social media data analysis; it provides more accurate information and classification performance. Common methods of sentiment analysis use machine learning or text information classification based on sentiment dictionary, but these methods do not effectively capture the subject of a sentence. Usually there are some keywords which called `target' are considered important emotional information in a sentence. The target words and its corresponding emotional vocabulary reflect the emotional situation of a sentence in different aspects, so we call it Aspect-Based Sentiment Analysis (ABSA). ABSA aims to predict fine-grained emotions in different aspects of comments or reviews. It is no longer a simple judgment of the emotional tendency of a sentence, good or bad, but a deeper analysis of the different aspects of emotion mentioned. Neural networks have also been widely used in sentiment analysis at the level of aspects. To explore it, we propose two methods to verify it. First, we try to use Bi-directional Long Short-Term Memory network (Bi-LSTM) to replace the existing Long Short-Term Memory networks (LSTM). The second method is to try to combine Convolutional Neural Network (CNN) with LSTM. We apply both methods to document-level data for pre-training to obtain parameters, through transfer learning we apply parameters to formal training. The experiments results show multiple neural network perform well in aspect-based sentiment analysis.
Recommendation Model Based on Deep Separated Graph Neural Network

The recommendation system [1] can quickly find the information that users are interested in from the complex information and recommend it to the user, and it has been widely used in web-based services. However, the current mainstream recommendation models all have data sparseness [2] and imbalance problems. To address these problems, we propose a deep separation graph neural network recommendation model (DSGNR) based on the idea of graph information aggregation. In the experiment, we used the classic recommendation system data set to verify and analyze the proposed DSGNR model and compared it with the seven mainstream recommendation models. Experimental results show that the proposed algorithm is generally better than the current mainstream recommendation methods in terms of accuracy and efficiency.
Corrections to “Joint Domain Adaptation Based on Adversarial Dynamic Parameter Learning” [Aug 21 714-723]

Due to the code optimization, we updated some experimental results. In addition, we updated the corresponding author.
Overlapping Oral Epithelial Cells Segmentation: Voronoi-Based Hybrid Active Contour Model

Oral cancer is a major healthcare problem and accounts for approximately 40% of cancer risk. It is evident that the detection of oral cancer at an early stage can markedly improve the survival rate. Though several invasive ‘gold standard’ methods continue to be practiced clinically, non-invasive detection modalities look promising for early oral cancer risk assessment. Exfoliative cytology is one such method in which the desquamated cells are collected from the oral epithelium and examined under microscope. However, the main challenge in automated quantification arises here on the formation of cellular clump during the smear preparation. In this chapter, the role of computational advances in segmentation of overlapping cells is illustrated. A computational model, based on Voronoi-based hybrid active contour method, is proposed for segmenting the overlapping oral epithelial cells.
A Novel Fruit Fly Optimization Algorithm with Vision Scanning Search and Extensive Learning Mechanism

The fruit fly optimization algorithm has drawn various attention to researchers and engineers, due to the simple theory and flexible frame. For solving the complex continuous optimization problems, an improved fruit fly algorithm based on vision scanning search and extensive learning mechanism is proposed in this paper. The vision scanning search strategy is used to scan the potential area by changing the search angle of swarm center. This strategy is utilized to guide the population to jump out the local trap. The extensive learning is using the knowledge of neighboring structure to increase the diversity of population for solving the non-separable issues. Furthermore, a new mutation strategy based on difference vector is proposed to improve the search efficiency of VLFOA. Testified in CEC 2017 benchmark problems, the results show that the VLFOA has a superior performance compared with the original FOA and the state of art variants of the FOA.
Evaluating Precision and Context in Image Captions

Captioning images is the process of generating the image description in a textual format that has wide applications in various domains. The research in this area is ongoing and seeking increasing attention as it merges the fields of computer vision and natural language processing. In the presented work, an image caption generation model consisting of a probabilistic and neural framework is built following an encoder-decoder scheme. The image is inputted to the deep learning classifier called Convolutional Neural Network (CNN) and to generate a set of sentences, a Long-Short Term Memory (LSTM) model is used. The captions generated by the proposed captioning model are compared over the other conventional models with respect to statistical analysis using standard and popular similarity metrics called Cosine Similarity, Precision and Recall. The results of evaluation of the predicted image captions compared to the actual captions are presented and the applicability of evaluation methods in context of image captioning is discussed and justified.
Chaotic image encryption algorithm based on higher and lower bit image operations

A chaotic image encryption algorithm based on bit level is proposed. The 8-bit grayscale matrix image is divided into a higher 4-bit matrix image and a lower 4-bit matrix image, which named higher bit image and lower bit image, respectively. Then the encrypted operations are performed on the higher bit and lower bit image. The operations include the mutual disturbing and element exchange operations between them, the scrambling of higher bit image and the diffusion of lower bit images. Simulations and performance analyses show that the proposed scheme has the advantages of large key space, strong system sensitivity, and excellent encryption security, especially good resistance to salt and pepper noise attacks.
Feasibility of Intelligent Techniques for Automated Positioning of Servomotors

Servomotors are widely used in computers, wall-climbing robots, antenna positioning, etc. Automated position control of servomotors is a major challenge due to non-linearity in the input dataset. Automated positioning of servomotors using soft computing techniques is proposed. Impact of Membership Functions (MFs) on the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) is studied. Triangular Membership function with 7 MFs result in less Root Mean Square Error (RMSE) for the three-digit approximated dataset used from the dissertation of Linus AlwalAloo. When comparing the two neural networks, Generalized Recurrent Neural Network (GRNN) provides the best results in terms of RMSE for servomotor position control.
BAYESIAN ESTIMATION AND AN APPLICATION OF GIBBS SAMPLING TECHNIQUE AND RANDOM WALK METROPOLIS - HASTING ALGORITHM IN TWO PHASE LINEAR REGRESSION MODEL

Here, in this research paper, we have applied the Gibbs Sampling Technique and RWM-H (Random Walk Metropolis - Hasting) Algorithm for the Bayesian Estimation of m, β1, β2 and 1/2. Also we have assumed that at some point of time say 'm', the co-efficient of regression changes from β1 to β2. Further, we have discussed about the effects of prior information on the Bayes estimates on the basis of the TPLR (Two Phase Linear Regression) Model with a Bayesian approach.
Live Container Migration via Pre-restore and Random Access Memory

Container technology is increasingly being used for virtualization due to its ability to isolate the operating environment of the program. In cloud computing environment, we need to migrate containers between different hosts for load balancing or downtime maintenance. However, during the migration process, the container will be temporarily shut down, and the service will be unavailable. Therefore, the time cost is an essential indicator to measure the quality of the migration process. To achieve live container migration, we propose a pre-restore method and a complete random access memory (RAM) based method to migrate containers. Extensive experiments validate the effectiveness of our methods in reducing downtime and improving the efficiency of container migration.
Multi-agent-Based Approach for Complex Industrial Process Modeling

A large number of components that interact with each other characterize complex systems. The modeling of such systems is generally based on a fine division of sub-assemblies of components of less complexity. Each component can be individually modeled and then simulated. This article deals with a top-down modeling method using multi-agent systems. The overall system is considered as a set of interacting agents. The behavior of the global system emerges from the behavior of its parts. This approach was applied to the liquefied natural gas treatment process (decarbonation) at an Algeria gas plant. The results of the simulation can be used to train recruits in this factory.